Investigating Improvements to Pedestrian

Crossings with an Emphasis on the 

Rectangular Rapid-Flashing Beacon 

PUBLICATION NO. FHWA-HRT-15-043 JUNE 2015

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
FOREWORD

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

Research Program is to improve safety and mobility for pedestrians, bicyclists and drivers to 

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

technologies, and the expansion of educational and safety programs. 

This report documents an FHWA project that investigated how characteristics of rapid-flashing 

beacons (e.g., shape, size, and brightness) affect the ability of drivers to detect people or objects 

along the roadway and the likelihood of drivers yielding to a pedestrian. 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-15-043

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

4. Title and Subtitle

Investigating Improvements to Pedestrian Crossings With an Emphasis 

on the Rectangular Rapid-Flashing Beacon

5. Report Date

June 2015

6. Performing Organization Code

7. Author(s)

Kay Fitzpatrick, Raul Avelar, Ingrid Potts, Marcus Brewer, James 

Robertson, Chris Fees, Jessica Hutton, Lindsay Lucas, and Karin Bauer

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 #4

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:

September 2010–March 2015

14. Sponsoring Agency Code

15. Supplementary Notes

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

16. Abstract

Several methods have been used to emphasize the presence of a pedestrian crossing, including supplementing 

signing with beacons or embedded light-emitting diodes. A device that has received national attention is the 

rectangular rapid-flashing beacon, but practitioners have asked whether the shape of the beacon plays a role in the 

effectiveness of this device. In the first phase of this project, researchers reviewed recent literature and pedestrian 

crash data to identify trends in pedestrian safety and in the effectiveness of crossing treatments. Researchers also 

conducted limited field observations at 10 crosswalks in 5 States, as a source of ideas for evaluating crossings in the 

second phase of the project. 

In phase II of the project, the research included a closed-course study and an open-road study to determine what 

characteristics of rapid-flashing beacons affected drivers’ ability to detect people or objects, as well as drivers’ 

likelihood of yielding to a pedestrian. The closed-course study included 71 participants who drove the course and 

viewed 8 beacon study assemblies, 9 distractor signs, and up to 11 roadside objects. The open-road study involved 

both rectangular beacons and circular beacons that were installed at 12 sites located in 4 cities from 3 States; both 

staged and nonstaged pedestrian crossings were documented. Although a slight difference was found between the 

average percent yielding to circular versus rectangular beacons (daytime: 67 to 59 percent; nighttime: 69 to 72 percent), 

the statistical evaluation determined that the shape of the beacon did not have a significant effect on drivers’ 

responses. However, a driver is more than three times as likely to yield when a beacon has been activated as when it 

has not been activated. Other variables that had an impact on driver yielding included beacon intensity (for 

nighttime) and city (yielding was higher in Flagstaff, AZ, compared with the other cities included in study), but not 

average daily traffic.

17. Key Words

Rectangular Rapid-Flashing Beacon, Circular RapidFlashing Beacon, RRFB, CRFB, Pedestrian Crossing, 

Driver Yielding to Pedestrians

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/about/contact.aspx

19. Security Classif.(of this report)

Unclassified

20. Security Classif.(of this page)

Unclassified

21. No. of Pages

266

22. Price

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

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 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

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) o

F Fahrenheit 5 (F-32)/9 Celsius o

C 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 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 mm2 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

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) o

C Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 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/in2

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

(Revised March 2003)
iii

TABLE OF CONTENTS

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

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

STUDY OBJECTIVE............................................................................................................... 2

SCOPE OF WORK .................................................................................................................. 3

STUDY APPROACH ............................................................................................................... 3

REPORT ORGANIZATION................................................................................................... 4

CHAPTER 2. LITERATURE REVIEW.................................................................................... 7

PREVIOUS OPEN-ROAD RESEARCH ON RECTANGULAR RAPID-FLASHING 

BEACONS ........................................................................................................................... 7

Original FHWA Study on RRFB............................................................................................ 7

2009 FHWA Study ................................................................................................................. 8

2009 Florida Study.................................................................................................................. 8

2011 Texas Study.................................................................................................................... 8

2011 Oregon Study ................................................................................................................. 9

2013 California Study........................................................................................................... 10

2013 Canada Study ............................................................................................................... 10

2014 Michigan Study............................................................................................................ 10

2014 Texas Study.................................................................................................................. 10

DRIVER DETECTION TO OBJECTS................................................................................ 13

PREVIOUS STUDIES ON LEGIBILITY DISTANCE FOR SYMBOL SIGNS ............. 14

CHAPTER 3. GATHER DATA ON PEDESTRIAN CRASHES........................................... 17

REVIEW OF CRASH DATABASES ................................................................................... 17

SUMMARY OF RESULTS ................................................................................................... 19

NHTSA FARS ...................................................................................................................... 20

NHTSA GES......................................................................................................................... 20

FHWA HSIS ......................................................................................................................... 21

RESULTS FROM ANALYSIS OF CRASH DATASETS.................................................. 23

NHTSA FARS ...................................................................................................................... 23

NHTSA GES......................................................................................................................... 34

CHAPTER 4. LOCAL FIELD OBSERVATIONS.................................................................. 67

OBSERVATIONS OF MIDBLOCK PEDESTRIAN CROSSINGS.................................. 67

Crosswalk 1: E. Bidwell Avenue Between Riley Avenue and Coloma Avenue in 

Folsom, CA..................................................................................................................... 68

Crosswalk 2: N. El Dorado Street Between E. Market Street and E. Weber Street in 

Stockton, CA................................................................................................................... 69

Crosswalk 3: N. El Dorado Street Between E. Market Street and E. Miner Street in 

Stockton, CA................................................................................................................... 71

Crosswalk 4: W. 80th Street Between Overland Park Drive and Marty Street in 

Overland Park, KS .......................................................................................................... 72

Crosswalk 5: W. 39th Avenue Between Rainbow Blvd and Cambridge Street in Kansas 

City, KS........................................................................................................................... 72

Crosswalk 6: Oak Street Between 51st Street and 52nd Street in Kansas City, MO............ 73

Crosswalk 7: Rockhill Road Between 50th Street and 51st Street in Kansas City, MO ...... 74
iv

Crosswalk 8: W. Walnut Street at N. Bullock Drive in Garland, TX................................... 75

Crosswalk 9: Barton Springs Road Between South 1st Street and Bouldin Avenue in 

Austin, TX....................................................................................................................... 76

Crosswalk 10: 23rd Street Near Crystal City Mall in Arlington, VA................................... 77

ASSESSMENT OF RESULTS .............................................................................................. 79

CHAPTER 5. CLOSED-COURSE STUDY............................................................................. 81

OVERVIEW............................................................................................................................ 81

Closed-Course Study Objective............................................................................................ 81

Study Approach Overview.................................................................................................... 82

COURSE DEVELOPMENT ................................................................................................. 84

Riverside Campus................................................................................................................. 84

Pedestrian Crossing Assemblies........................................................................................... 85

Assemblies Selected for Discomfort Glare Study................................................................. 91

Flash Pattern for Assemblies ................................................................................................ 93

Brightness of Beacons/LEDs................................................................................................ 95

Stop Signs and Distractor Signs Selected for Driving Study................................................ 98

Objects .................................................................................................................................. 98

Site Selection for Study Assemblies, Distractor Signs, Stop Signs, and Objects............... 103

Route Preparation................................................................................................................ 104

Practitioner Review............................................................................................................. 107

DATA COLLECTION......................................................................................................... 107

Study Periods...................................................................................................................... 107

Participants.......................................................................................................................... 109

Tasks................................................................................................................................... 109

Instrumented Vehicle .......................................................................................................... 110

Participant Intake ................................................................................................................ 110

Response Time Testing....................................................................................................... 111

Vehicle Review................................................................................................................... 112

First Lap of Driving Course................................................................................................ 112

Second Lap of Driving Course ........................................................................................... 113

Discomfort Glare ................................................................................................................ 113

DATA REDUCTION............................................................................................................ 114

Participant Demographics................................................................................................... 114

Response Time.................................................................................................................... 114

Detection Distance .............................................................................................................. 117

Box Plots............................................................................................................................. 117

Data Cleaning...................................................................................................................... 118

RESULTS .............................................................................................................................. 118

Detection Distance—Light ................................................................................................. 118

Detection Distance—Sign................................................................................................... 118

Legibility Distance.............................................................................................................. 119

Detection Distance—Object ............................................................................................... 127

Accuracy of Detecting an Object........................................................................................ 139

Discomfort Glare ................................................................................................................ 148
v

CHAPTER 6. OPEN-ROAD STUDY ..................................................................................... 159

STUDY DEVELOPMENT................................................................................................... 159

Study Sites .......................................................................................................................... 159

Study Assemblies................................................................................................................ 160

Rotation............................................................................................................................... 160

Brightness of Beacons......................................................................................................... 160

DATA COLLECTION......................................................................................................... 164

Study Periods...................................................................................................................... 164

Staged Pedestrian Protocol ................................................................................................. 165

Video................................................................................................................................... 165

Brightness of Beacons......................................................................................................... 165

DATA REDUCTION............................................................................................................ 167

Driver Yielding ................................................................................................................... 167

Brightness ........................................................................................................................... 168

FINDINGS............................................................................................................................. 170

Driver Yielding Findings.................................................................................................... 170

Brightness Findings ............................................................................................................ 172

RESULTS .............................................................................................................................. 176

Comparison of CRFB to RRFB.......................................................................................... 176

Comparison of CRFB to RRFB Considering Beacon Brightness ...................................... 177

Comparison of Driver Yielding When Beacon Activated to Beacon Not Activated.......... 180

Influence of Traffic Volume on Driver Yielding................................................................ 183

CHAPTER 7. SUMMARY/CONCLUSIONS, DISCUSSION, AND FUTURE 

RESEARCH NEEDS.......................................................................................................... 187

OVERVIEW.......................................................................................................................... 187

SUMMARY OF PHASE I FINDINGS ............................................................................... 187

Findings From Literature Review....................................................................................... 187

Review of Pedestrian Crash Data........................................................................................ 187

Local Field Observations.................................................................................................... 189

Selection of Studies for Phase II......................................................................................... 190

SUMMARY AND CONCLUSIONS FROM CLOSED-COURSE STUDY.................... 190

Driving Portion ................................................................................................................... 191

Discomfort Glare Portion.................................................................................................... 191

Participants.......................................................................................................................... 192

Results................................................................................................................................. 192

SUMMARY AND CONCLUSIONS FROM OPEN-ROAD STUDY .............................. 194

Shape................................................................................................................................... 194

Activation............................................................................................................................ 194

Traffic Volume.................................................................................................................... 195

Results................................................................................................................................. 195

DISCUSSION........................................................................................................................ 195

FUTURE RESEARCH NEEDS .......................................................................................... 196

Appropriate Use of Rapid-Flashing Beacon Assemblies on Only One Side of the 

Roadway Approach....................................................................................................... 196

When Rapid-Flashing Beacons Should Be Dimmed, and by How Much .......................... 197

Appropriate Brightness Level of Rapid-Flashing Beacons................................................. 197
vi

Appropriate Installation of Rapid-Flashing Beacon Assemblies Overhead Rather 

Than on the Roadside.................................................................................................... 197

Guidance on Selection of Appropriate Pedestrian Crossing Treatment for a Particular 

Location ........................................................................................................................ 198

National Education Campaign on the Rapid-Flashing Beacon........................................... 199

Minimum Number of Pedestrians to Warrant a Pedestrian Treatment............................... 199

Number of Pedestrians Induced as a Result of Installation of Selected Pedestrian 

Treatments..................................................................................................................... 200

Drivers’ Search Patterns Near Flashing Beacons ............................................................... 200

Pedestrians’ Attitude Toward Using Treatments................................................................ 200

Influence of Traffic Volume on Driver Yielding................................................................ 200

Estimating Pedestrian Exposure ......................................................................................... 201

APPENDIX: PEDESTRIAN TREATMENTS....................................................................... 203

ADVANCE STOP OR YIELD LINE AND SIGN ............................................................. 203

BARRIER—MEDIAN ......................................................................................................... 204

BARRIER—ROADSIDE/SIDEWALK.............................................................................. 205

BUS STOP LOCATION ...................................................................................................... 206

CIRCULAR BEACONS....................................................................................................... 207

CROSSWALK MARKING PATTERNS........................................................................... 208

CURB EXTENSIONS .......................................................................................................... 210

FLAGS ................................................................................................................................... 211

ILLUMINATION ................................................................................................................. 212

IN-ROADWAY WARNING LIGHTS AT CROSSWALKS............................................ 214

IN-STREET PEDESTRIAN CROSSING SIGNS ............................................................. 218

MARKED CROSSWALKS................................................................................................. 222

MOTORIST WARNING SIGNS ........................................................................................ 223

OVERPASSES AND UNDERPASSES............................................................................... 224

PEDESTRIAN HYBRID BEACON (ALSO KNOWN AS HAWK)................................ 226

PUFFIN.................................................................................................................................. 228

RAISED CROSSWALKS .................................................................................................... 229

RECTANGULAR RAPID-FLASHING BEACON........................................................... 229

REFUGE ISLANDS ............................................................................................................. 229

ROAD DIETS........................................................................................................................ 231

SIDEWALKS ........................................................................................................................ 233

ZIGZAG LINES ................................................................................................................... 233

MULTIPLE TREATMENTS .............................................................................................. 234

ACKNOWLEDGMENTS........................................................................................................ 239

REFERENCES.......................................................................................................................... 241
vii

LIST OF FIGURES

Figure 1. Photo. School crosswalk with RRFBs in Garland, TX ................................................... 9

Figure 2. Photo. Study site from TxDOT study showing overhead RRFB installation................ 11

Figure 3. Graph. RRFB: driver yielding to posted speed limit plot from 2014 Texas study........ 12

Figure 4. Graph. RRFB: driver yielding to total crossing distance plot from 2014 Texas 

study.................................................................................................................................. 12

Figure 5. Photo. Unsignalized pedestrian crosswalk in Folsom, CA............................................ 68

Figure 6. Photo. Unsignalized pedestrian crosswalk in Folsom, CA, pedestrian view ................ 68

Figure 7. Photo. Unsignalized pedestrian crosswalk in Folsom, CA, pedestrian pushbutton ...... 69

Figure 8. Photo. Unsignalized pedestrian crosswalk in Stockton, CA. ........................................ 70

Figure 9. Photo. Unsignalized pedestrian crosswalk in Stockton, CA, pedestrian view .............. 70

Figure 10. Photo. Unsignalized pedestrian crosswalk in Stockton, CA, pedestrian 

pushbutton......................................................................................................................... 70

Figure 11. Photo. Unsignalized pedestrian crosswalk in Stockton, CA. ...................................... 71

Figure 12. Photo. Unsignalized pedestrian crosswalk in Overland Park, KS............................... 72

Figure 13. Photo. Unsignalized pedestrian crosswalk in Kansas City, KS................................... 73

Figure 14. Photo. Signalized pedestrian crosswalk in Kansas City, MO. .................................... 74

Figure 15. Photo. Another view of signalized pedestrian crosswalk in Kansas City, MO........... 74

Figure 16. Photo. Unsignalized pedestrian crosswalk in Kansas City, MO. ................................ 75

Figure 17. Photo. A different view of the crosswalk in Garland, TX........................................... 76

Figure 18. Photo. Crosswalk on Barton Springs Road in Austin, TX .......................................... 77

Figure 19. Photo. Example of pedestrians outside of crosswalk at site on 23rd Street ................ 78

Figure 20. Photo. Colored pavement markings used to promote local area. ................................ 78

Figure 21. Diagram. Test route for Riverside study along with signs and objects positions........ 83

Figure 22. Diagram. Layout for the discomfort glare study ......................................................... 84

Figure 23. Photo. C-A12, lap A study assembly........................................................................... 85

Figure 24. Photo. C-A12, lap B study assembly........................................................................... 86

Figure 25. Photo. C-B12, lap A study assembly........................................................................... 86

Figure 26. Photo. C-B12, lap B study assembly........................................................................... 86

Figure 27. Photo. C-B8, lap A study assembly............................................................................. 87

Figure 28. Photo. C-B8, lap B study assembly............................................................................. 87

Figure 29. Photo. C-V12, lap A study assembly........................................................................... 87

Figure 30. Photo. C-V12, lap B study assembly........................................................................... 88

Figure 31. Photo. R-A, lap A study assembly............................................................................... 88

Figure 32. Photo. R-A, lap B study assembly............................................................................... 88

Figure 33. Photo. R-B, lap A study assembly............................................................................... 89

Figure 34. Photo. R-B, lap B study assembly............................................................................... 89

Figure 35. Photo. LED, lap A study assembly.............................................................................. 89

Figure 36. Photo. LED, lap B study assembly.............................................................................. 90

Figure 37. Photo. WO-B, lap A study assembly........................................................................... 90

Figure 38. Photo. WO-B, lap B study assembly........................................................................... 90

Figure 39. Photo. Discomfort glare assembly with circular beacons. .......................................... 92

Figure 40. Photo. Discomfort glare assembly with embedded LEDs........................................... 92

Figure 41. Photo. View of discomfort glare course...................................................................... 93

Figure 42. Graph. Flash pattern for rapid-flashing beacons ......................................................... 94
viii

Figure 43. Graph. LED-embedded sign flash pattern (uses same five-pulse pattern as that 

used by the right beacon in a rapid flash pattern). ............................................................ 94

Figure 44. Photo. LED-embedded sign mounted on goniometer ................................................. 95

Figure 45. Photo. Mounted 12-inch circular beacon..................................................................... 95

Figure 46. Graph. Example peak luminous intensity and optical power calculations.................. 96

Figure 47. Graph. Optical power measurements for driving study assemblies compared with 

discomfort glare study controller settings (one setting in driving study). ........................ 97

Figure 48. Graph. 95th percentile intensity measurements for driving study assemblies 

compared with discomfort glare study controller settings (one setting in driving 

study)................................................................................................................................. 97

Figure 49. Photo. Small gray box on course............................................................................... 100

Figure 50. Photo. Close-up of small gray box. ........................................................................... 101

Figure 51. Photo. Trash can on course........................................................................................ 101

Figure 52. Photo. Pedestrian on course....................................................................................... 102

Figure 53. Photo. Base for signs without beacons/LEDs............................................................ 105

Figure 54. Photo. Base for signs with beacons/LEDs................................................................. 105

Figure 55. Photo. Example of sign change. ................................................................................ 106

Figure 56. Photo. Another example of sign change.................................................................... 107

Figure 57. Graph. Measured response times by participant........................................................ 116

Figure 58. Graph. Response times by participant after removing outliers. ................................ 116

Figure 59. Diagram. Box plot details.......................................................................................... 117

Figure 60. Graph. Box plots for nighttime legibility distance for assemblies with pedestrian 

crossing sign for young participants............................................................................... 124

Figure 61. Graph. Box plots for nighttime legibility distance for assemblies with pedestrian 

crossing sign for old participants. ................................................................................... 125

Figure 62. Graph. Box plot of daytime object detection distance by upstream condition.......... 130

Figure 63. Graph. Box plot of nighttime object detection distance by upstream condition ....... 130

Figure 64. Graph. Percent of response for discomfort glare study—C-B8 at position 1............ 149

Figure 65. Graph. Percent of response for discomfort glare study—C-B8 at position 2............ 149

Figure 66. Graph. Percent of response for discomfort glare study—LED at position 1............. 150

Figure 67. Graph. Percent of response for discomfort glare study—LED at position 2............. 150

Figure 68. Equation. Basic form of the Cumulative Logit model. ............................................. 150

Figure 69. Equation. Probability of unbearable brightness......................................................... 151

Figure 70. Equation. Basic form of the odds ratio for events A and B....................................... 151

Figure 71. Equation. Probability of unbearable discomfort glare based on optical power......... 154

Figure 72. Equation. Probability of unbearable discomfort glare based on intensity................. 154

Figure 73. Graph. Older drivers’ probability of unbearable discomfort glare by optical 

power and time of day for LED-embedded signs at 250 ft............................................. 155

Figure 74. Graph. Older drivers’ probability of unbearable discomfort glare by 95th percentile 

intensity and time of day for LED-embedded signs at 250 ft......................................... 156

Figure 75. Graph. Typical hourly distribution used to convert 1-h volume into CDT............... 160

Figure 76. Photo. Rectangular beacons used at CS-01. .............................................................. 161

Figure 77. Photo. Circular beacons used at CS-02. .................................................................... 161

Figure 78. Photo. Circular beacons used at AU-01 .................................................................... 161

Figure 79. Photo. Rectangular beacons used at AU-02. ............................................................. 161

Figure 80. Photo. Circular beacons used at MK-04.................................................................... 162
ix

Figure 81. Photo. Circular beacons used at MK-05.................................................................... 162

Figure 82. Photo. Circular beacons used at MK-06.................................................................... 162

Figure 83. Photo. Rectangular beacons used at MK-07.............................................................. 162

Figure 84. Photo. Rectangular beacons used at MK-08.............................................................. 163

Figure 85. Photo. Circular beacons used at FG-01. .................................................................... 163

Figure 86. Photo. Circular beacons used at FG-02. .................................................................... 163

Figure 87. Photo. Rectangular beacons used at FG-03............................................................... 163

Figure 88. Equation. Yielding rate for a single crossing and average yielding rate for a site .... 168

Figure 89. Equation. Average luminance energy........................................................................ 168

Figure 90. Equation. Optical power............................................................................................ 168

Figure 91. Graph. Example of correctly captured and graphed flash cycle showing five 

pulses of light (0.0 to 0.4 time) followed by two pulses of light (0.4 to 0.8 time)......... 169

Figure 92. Equation. Average intensity ...................................................................................... 172

Figure 93. Equation. Optical power by approach. ...................................................................... 172

Figure 94. Equation. Average intensity by approach.................................................................. 173

Figure 95. Equation. Optical power for a site............................................................................. 173

Figure 96. Equation. Average intensity for a site. ...................................................................... 173

Figure 97. Graph. Plot of average optical power by beacon shape and site. .............................. 175

Figure 98. Graph. Plot of average intensity by beacon shape and site. ...................................... 175

Figure 99. Graph. Driver yielding compared with beacon brightness intensity for day............. 179

Figure 100. Graph. Driver yielding compared with beacon brightness intensity for night ........ 179

Figure 101. Graph. Predicted percent of driver yielding by 1-min volume counts. ................... 185

Figure 102. Graph. Example of a graph generated from NCHRP 562/TCRP 112 

methodology (function of walking speed, crossing distance, and other variables) 

that could be used to determine pedestrian treatment..................................................... 199

Figure 103. Diagram. Examples of crosswalk markings (figure proposed to replace existing 

MUTCD figure 3B-19) ................................................................................................... 209

Figure 104. Photo. Four-lane configuration before road diet ..................................................... 232

Figure 105. Photo. Three-lane configuration after road diet ...................................................... 232

Figure 106. Diagram. Schematic of zig-zag pavement marking design ..................................... 234
x

LIST OF TABLES

Table 1. Measures of effectiveness for RRFBs with pedestrian crossing signs, Miami, FL 

(compiled from reference 4) ............................................................................................... 8

Table 2. RRFB total driver yielding model results....................................................................... 13

Table 3. Assessment of screening questions for national and State databases. ............................ 19

Table 4. Summary of FARS pedestrian fatalities data (2005–2009)............................................ 24

Table 5. FARS midblock pedestrian fatalities by traffic control device (2005–2009)................. 25

Table 6. FARS midblock pedestrian fatalities by pedestrian actions (2005–2009)...................... 26

Table 7. FARS midblock pedestrian fatalities by pedestrian age (2005–2009). .......................... 27

Table 8. FARS midblock pedestrian fatalities by pedestrian gender (2005–2009). ..................... 27

Table 9. FARS midblock pedestrian fatalities by number of lanes crossed (2005–2009)............ 28

Table 10. FARS midblock pedestrian fatalities by presence and type of median 

(2005–2009)...................................................................................................................... 28

Table 11. FARS midblock pedestrian fatalities by posted speed limit (2005–2009). .................. 29

Table 12. Midblock pedestrian fatalities by weather condition (2005–2009). ............................. 29

Table 13. FARS midblock pedestrian fatalities by road surface condition (2005–2009)............. 30

Table 14. FARS midblock pedestrian fatalities by light condition (2005–2009)......................... 30

Table 15. FARS midblock pedestrian fatalities by hour of day (2005–2009).............................. 31

Table 16. FARS midblock pedestrian fatalities by day of week (2005–2009)............................. 31

Table 17. FARS midblock pedestrian fatalities by State (2005–2009). ....................................... 32

Table 18. FARS midblock pedestrian fatalities for FHWA pedestrian safety focus States and 

cities (2005–2009) ............................................................................................................ 34

Table 19. Summary of GES pedestrian crash data (2005–2009).................................................. 35

Table 20. GES midblock pedestrian crashes by injury severity (2005–2008).............................. 35

Table 21. GES midblock pedestrian crashes by traffic control device (2005–2009) ................... 35

Table 22. GES midblock pedestrian crashes by pedestrian action (2005–2009).......................... 36

Table 23. GES midblock pedestrian crashes by pedestrian age (2005–2009).............................. 36

Table 24. GES midblock pedestrian crashes by pedestrian gender (2005–2009)......................... 37

Table 25. GES midblock pedestrian crashes by number of travel lanes crossed (2005–2009).... 37

Table 26. GES midblock pedestrian crashes by presence of median (2005–2009)...................... 37

Table 27. GES midblock pedestrian crashes by posted speed limit (2005–2009)........................ 38

Table 28. GES midblock pedestrian crashes by weather condition (2005–2009). ....................... 38

Table 29. GES midblock pedestrian crashes by road surface condition (2005–2009). ................ 38

Table 30. GES midblock pedestrian crashes by hour of day (2005–2009). ................................. 39

Table 31. GES midblock pedestrian crashes by light condition (2005–2009). ............................ 39

Table 32. Summary of pedestrian crash data for California State highways (2006–2008). ......... 40

Table 33. Midblock pedestrian crashes on California State highways by pedestrian location 

(2006–2008)...................................................................................................................... 40

Table 34. Midblock pedestrian crashes on California State highways by injury severity (2006–

2008) ................................................................................................................................. 40

Table 35. Midblock pedestrian crashes on California State highways by presence of median 

(2006–2008)...................................................................................................................... 41

Table 36. Midblock pedestrian crashes on California State highways by traffic control device 

operating (2006–2008)...................................................................................................... 41

Table 37. Midblock pedestrian crashes on California State highways by collision factor (2006–

2008) ................................................................................................................................. 42
xi

Table 38. Midblock pedestrian crashes on California State highways by weather condition 

(2006–2008)...................................................................................................................... 42

Table 39. Midblock pedestrian crashes on California State highways by vehicle type at fault 

(2006–2008)...................................................................................................................... 43

Table 40. Midblock pedestrian crashes on California State highways by road surface 

condition (2006–2008)...................................................................................................... 43

Table 41. Midblock pedestrian crashes on California State highways by light condition 

(2006–2008)...................................................................................................................... 43

Table 42. Midblock pedestrian crashes on California State highways by hour of day 

(2006-2008)....................................................................................................................... 44

Table 43. Midblock pedestrian crashes on California State highways by day of the week 

(2006–2008)...................................................................................................................... 44

Table 44. Summary of pedestrian crash data for Minnesota State highways (2003–2007). ........ 45

Table 45. Midblock pedestrian crashes on Minnesota State highways by pedestrian location 

(2003–2007)...................................................................................................................... 45

Table 46. Midblock pedestrian crashes on Minnesota State highways by injury severity 

(2003–2007)...................................................................................................................... 46

Table 47. Midblock pedestrian crashes on Minnesota State highways by traffic control device 

operating (2003–2007)...................................................................................................... 46

Table 48. Midblock pedestrian crashes on Minnesota State highways by weather condition 

(2003–2007)...................................................................................................................... 47

Table 49. Midblock pedestrian crashes on Minnesota State highways by road surface 

condition (2003–2007)...................................................................................................... 47

Table 50. Midblock pedestrian crashes on Minnesota State highways by light condition 

(2003–2007)...................................................................................................................... 47

Table 51. Midblock pedestrian crashes on Minnesota State highways by hour of day (2003–

2007) ................................................................................................................................. 48

Table 52. Midblock pedestrian crashes on Minnesota State highways by day of the week 

(2003–2007)...................................................................................................................... 48

Table 53. Summary of pedestrian crash data for North Carolina State highways 

(2005–2008)...................................................................................................................... 49

Table 54. Midblock pedestrian crashes on North Carolina State highways by pedestrian 

location (2005–2008)........................................................................................................ 49

Table 55. Midblock pedestrian crashes on North Carolina State highways by injury severity 

(2005–2008)...................................................................................................................... 49

Table 56. Midblock pedestrian crashes on North Carolina State highways by pedestrian 

action (2005–2008)........................................................................................................... 50

Table 57. Midblock pedestrian crashes on North Carolina State highways by contributing 

factor (2005–2008)............................................................................................................ 50

Table 58. Midblock pedestrian crashes on North Carolina State highways by weather 

condition (2005–2008)...................................................................................................... 51

Table 59. Midblock pedestrian crashes on North Carolina State highways by road surface 

condition (2005–2008)...................................................................................................... 51

Table 60. Midblock pedestrian crashes on North Carolina State highways by light condition 

(2005–2008)...................................................................................................................... 51
xii

Table 61. Midblock pedestrian crashes on North Carolina State highways by day of the week 

(2005–2008)...................................................................................................................... 52

Table 62. Midblock pedestrian crashes on North Carolina State highways by hour of day 

(2005–2008)...................................................................................................................... 52

Table 63. Summary of pedestrian crash data for Ohio State highways (2005–2009) .................. 53

Table 64. Midblock pedestrian crashes on Ohio State highways by pedestrian location 

(2005–2009)...................................................................................................................... 53

Table 65. Midblock pedestrian crashes on Ohio State highways by injury severity 

(2005–2009)...................................................................................................................... 53

Table 66. Midblock pedestrian crashes on Ohio State highways by traffic control device 

(2005–2009)...................................................................................................................... 54

Table 67. Midblock pedestrian crashes on Ohio State highways by pedestrian action 

(2005–2009)...................................................................................................................... 54

Table 68. Midblock pedestrian crashes on Ohio State highways by number of lanes crossed 

(2005–2009)...................................................................................................................... 55

Table 69. Midblock pedestrian crashes on Ohio State highways by presence of median 

(2005–2009)...................................................................................................................... 55

Table 70. Midblock pedestrian crashes on Ohio State highways by weather condition 

(2005–2009)...................................................................................................................... 55

Table 71. Midblock pedestrian crashes on Ohio State highways by road surface condition 

(2005–2009)...................................................................................................................... 56

Table 72. Midblock pedestrian crashes on Ohio State highways by light condition 

(2005–2009)...................................................................................................................... 56

Table 73. Midblock pedestrian crashes on Ohio State highways by day of the week 

(2005–2009)...................................................................................................................... 56

Table 74. Midblock pedestrian crashes on Ohio State highways by hour of the day 

(2005–2009)...................................................................................................................... 57

Table 75. Summary of pedestrian crash data for Texas roadways (2003–2009).......................... 58

Table 76. Midblock pedestrian crashes on Texas roadways by pedestrian location 

(2003–2009)...................................................................................................................... 58

Table 77. Midblock pedestrian crashes on Texas roadways by injury severity (2003–2009)...... 58

Table 78. Midblock pedestrian crashes on Texas roadways by traffic control device 

(2003–2009)...................................................................................................................... 59

Table 79. Midblock pedestrian crashes on Texas roadways by weather condition 

(2003–2009)...................................................................................................................... 59

Table 80. Midblock pedestrian crashes on Texas roadways by road surface condition (2003–

2009) ................................................................................................................................. 60

Table 81. Midblock pedestrian crashes on Texas roadways by light condition (2003–2009)...... 60

Table 82. Midblock pedestrian crashes on Texas roadways by hour of day (2003–2009)........... 61

Table 83. Midblock pedestrian crashes on Texas roadway by day of week (2003–2009)........... 61

Table 84. Summary of pedestrian crash data for Washington State highways (2005–2008). ...... 62

Table 85. Midblock pedestrian crashes on Washington State highways by pedestrian 

location (2005–2008)........................................................................................................ 62

Table 86. Midblock pedestrian crashes on Washington State highways by injury severity 

(2005–2008)...................................................................................................................... 63
xiii

Table 87. Midblock pedestrian crashes on Washington State highways by contributing factor 

(2005–2008)...................................................................................................................... 63

Table 88. Midblock pedestrian crashes on Washington State highways by presence of 

median (2005–2008)......................................................................................................... 63

Table 89. Midblock pedestrian crashes on Washington State highways by road surface 

condition (2005–2008)...................................................................................................... 64

Table 90. Midblock pedestrian crashes on Washington State highways by light condition 

(2005–2008)...................................................................................................................... 64

Table 91. Midblock pedestrian crashes on Washington State highways by day of the week 

(2005–2008)...................................................................................................................... 64

Table 92. Midblock pedestrian crashes on Washington State highways by hour of the day 

(2005–2008)...................................................................................................................... 65

Table 93. Study assemblies used in closed-course Riverside track study. ................................... 91

Table 94. Rapid flash pattern........................................................................................................ 93

Table 95. LED-embedded sign flash pattern. ............................................................................... 94

Table 96. Distractors and stop signs used in closed-course study ................................................ 99

Table 97. Object location on course ........................................................................................... 100

Table 98. Study device and sign face by object position (view distance in ft) used with 

pedestrians....................................................................................................................... 102

Table 99. Order of device presentation on closed course. .......................................................... 104

Table 100. Participant time in study. .......................................................................................... 108

Table 101. Distribution of participants. ...................................................................................... 109

Table 102. Demographic information for 71 participants. ......................................................... 115

Table 103. Response time by experimenter................................................................................ 115

Table 104. Sign detection distance by sign type without attempts to control for external 

elements such as viewing distance that could affect results. .......................................... 119

Table 105. Legibility distance by assembly type and sign face.................................................. 120

Table 106. Linear mixed-effects model results using all assemblies for daytime legibility 

distance ........................................................................................................................... 122

Table 107. ANOVA results using all assemblies for daytime legibility distance. ..................... 122

Table 108. Daytime legibility distance multiple comparison using all assemblies data. ........... 122

Table 109. Linear mixed-effects model results using all assemblies for nighttime legibility 

distance ........................................................................................................................... 123

Table 110. ANOVA results using all assemblies for nighttime legibility distance. ................... 124

Table 111. Nighttime legibility distance multiple comparisons using simultaneous tests for 

general linear hypostheses. ............................................................................................. 124

Table 112. Linear mixed-effects model results for assemblies with pedestrian crossing sign 

for nighttime legibility distance ...................................................................................... 125

Table 113. Object detection distance for the pedestrian by upstream device and sign face....... 128

Table 114. Object detection distance for the trash can by upstream device and sign face......... 129

Table 115. Object detection distance for the box by upstream device and sign face. ................ 129

Table 116. Model for daytime object detection distance considering upstream condition ........ 131

Table 117. ANOVA results using upstream conditions daytime object detection distance. ...... 131

Table 118. Model for daytime object detection distance focusing on upstream assembly......... 132

Table 119. ANOVA results for daytime detection distance focusing on upstream assembly.... 132
xiv

Table 120. Daytime object detection distance, multiple comparisons by object type and age 

group ............................................................................................................................... 133

Table 121. Daytime object detection distance, multiple comparisons by sign family ............... 133

Table 122. Daytime object detection distance, multiple comparisons to reference assembly 

by assembly type............................................................................................................. 133

Table 123. Daytime object detection distance, other multiple comparisons by assembly 

type.................................................................................................................................. 134

Table 124. Daytime object detection distance for beacon placement......................................... 134

Table 125. Model for nighttime object detection distance considering upstream condition...... 135

Table 126. ANOVA results for nighttime object detection distance considering upstream. 

condition. ........................................................................................................................ 135

Table 127. Model for nighttime object detection distance considering upstream assembly ...... 136

Table 128. ANOVA results using for nighttime object detection distance considering 

upstream assembly.......................................................................................................... 136

Table 129. Nighttime object detection distance, multiple comparisons by age group ............... 136

Table 130. Nighttime object detection distance, multiple comparisons by object type ............. 136

Table 131. Nighttime object detection distance, multiple comparisons by sign family............. 137

Table 132. Nighttime object detection distance, multiple comparisons to reference 

assembly by assembly type............................................................................................. 137

Table 133. Nighttime object detection distance, other multiple comparisons by assembly 

type.................................................................................................................................. 138

Table 134. Nighttime object detection distance for beacon placement. ..................................... 138

Table 135. Object percent missed by previous device group, object type, and light 

condition. ........................................................................................................................ 140

Table 136. Object percent missed by age group, object type, and light condition. .................... 141

Table 137. Model for daytime object detection accuracy considering upstream condition ....... 142

Table 138. Model for daytime object detection accuracy considering upstream assembly........ 143

Table 139. Daytime object detection accuracy, multiple comparisons by object type............... 143

Table 140. Daytime object detection accuracy, multiple comparisons by age group................. 143

Table 141. Daytime object detection accuracy, multiple comparisons by sign family .............. 143

Table 142. Daytime object detection accuracy, multiple comparisons to reference assembly 

by assembly type............................................................................................................. 144

Table 143. Daytime object detection accuracy, other multiple comparisons by assembly 

type.................................................................................................................................. 144

Table 144. Daytime object detection accuracy for beacon placement........................................ 144

Table 145. Model for nighttime object detection accuracy considering upstream condition ..... 145

Table 146. Model for nighttime object detection accuracy considering upstream assembly ..... 146

Table 147. Nighttime object detection accuracy, multiple comparisons by object type ............ 146

Table 148. Nighttime object detection accuracy, multiple comparisons by age group. ............. 146

Table 149. Nighttime object detection accuracy, multiple comparisons by sign family............ 146

Table 150. Nighttime object detection accuracy, multiple comparisons to reference 

assembly by assembly type............................................................................................. 147

Table 151. Nighttime object detection accuracy, other multiple comparisons by assembly 

type.................................................................................................................................. 147

Table 152. Nighttime object detection accuracy for beacon placement ..................................... 147

Table 153. Average speed of vehicle when object was detected or missed. .............................. 148
xv

Table 154. Discomfort glare variable names and descriptions................................................... 152

Table 155. Model specification and selection using Akaike Information Criterion (AIC). ....... 152

Table 156. Cumulative logit model with C_Level as a measure of brightness. ......................... 153

Table 157. Cumulative logit model with OP_One as a measure of brightness. ......................... 153

Table 158. Cumulative logit model with INT_One as a measure of brightness......................... 154

Table 159. Unbearable discomfort glare for 10th, 15th, and 50th percentiles for 

LED-embedded signs at 250 ft. ...................................................................................... 156

Table 160. Unbearable discomfort glare percentiles at SAE minimum and three times the 

SAE minimum for LED-embedded signs at 250 ft......................................................... 156

Table 161. Study site characteristics........................................................................................... 159

Table 162. Installation dates and dates of data collection. ......................................................... 164

Table 163. Photometric terminology. ......................................................................................... 166

Table 164. Equipment list and purpose....................................................................................... 166

Table 165. Data collection dates by site and city........................................................................ 166

Table 166. Daytime driver yielding rate by site and assembly................................................... 171

Table 167. Nighttime driver yielding rate by site and assembly ................................................ 172

Table 168. Average optical power and intensity by crossing ..................................................... 174

Table 169. Device brightness at each site................................................................................... 174

Table 170. GLMM results comparing CRFB to RRFB.............................................................. 177

Table 171. GLMM results comparing CRFB to RRFB when beacon brightness data are 

available .......................................................................................................................... 178

Table 172. Daytime effect of intensity on driver yielding for a theoretical site and for the 

raw data averages............................................................................................................ 179

Table 173. Nighttime effect of intensity on driver yielding for a theoretical site and for the 

raw data averages............................................................................................................ 180

Table 174. Multiple comparisons for natural light effect on driver yielding by intensity.......... 180

Table 175. Number of activated and non-activated crossings by city and site........................... 181

Table 176. Stepwise elimination procedure results. ................................................................... 181

Table 177. GLMM results comparing driver yielding rates between activated and 

non-activated beacons..................................................................................................... 182

Table 178. Predicted driver yielding rates by city and site......................................................... 183

Table 179. Odds ratio results for beacon activation. .................................................................. 183

Table 180. One-min volume count statistics at crossings with RRFBs...................................... 184

Table 181. Overview of driver yielding results from several RRFB studies.............................. 188

Table 182. Findings for 2013 Santa Monica, CA study ............................................................. 188

Table 183. Pedestrian treatments for unsignalized locations...................................................... 203

Table 184. Speed changes due to bulbouts................................................................................. 211

Table 185. Percentage of motorists yielding to pedestrians at bulbout crosswalks.................... 211

Table 186. Percentage of motorists stopping for staged pedestrians at bulbout crosswalks ...... 211

Table 187. Crash effects of providing sodium floodlights at pedestrian crossings in Perth, 

Australia.......................................................................................................................... 212

Table 188. Effects of crosswalk illumination on nighttime pedestrian crashes in Israel............ 213

Table 189. Results for “smart lighting” pedestrian safety MOEs............................................... 213

Table 190. Results for “smart lighting” motorist safety MOEs.................................................. 214

Table 191. Driver yielding at in-street installations.................................................................... 219

Table 192. Evaluation results on in-street pedestrian crossing signs.......................................... 221
xvi

Table 193. Effectiveness of pedestrian treatments at unsignalized locations............................. 224

Table 194. Comparison of crashes before and after installation of pedestrian overpasses in 

Tokyo .............................................................................................................................. 225

Table 195. Comparison of vehicle speeds at raised crosswalks ................................................. 229

Table 196. Pedestrians at raised crosswalks for whom motorists stopped ................................. 229

Table 197. Pedestrians at refuge islands for whom motorists yielded........................................ 230

Table 198. Drivers yielding to pedestrians at median refuge islands......................................... 231

Table 199. Trapped pedestrians at offset median openings........................................................ 231

Table 200. Drivers yielding to pedestrians at offset median openings....................................... 231

Table 201. Results of EB analysis on four-lane to three-lane road diets.................................... 232
xvii

LIST OF ABBREVIATIONS

ADT Average Daily Traffic (vehicles/day)

AIC Akaike’s Information Criterion

ANOVA Analysis of Variance

ASCII American Standard Code for Information Interchange

C-A12 For the closed-course study, two circular 12-inch beacons located above the sign

C-B12 For the closed-course study, two circular 12-inch beacons located below the sign

C-B8 For the closed-course study, two circular 8-inch beacons located below the sign

CDT Calculated Daily Traffic (vehicles per day) (determined using 1-hour count and 

national hourly traffic distribution data for non-freeway roads with no, low, and 

moderate congestion)

CMF Crash Modification Factor

CRFB Circular Rapid-Flashing Beacon

C-V12 For the closed-course study, one circular 12-inch beacon located above the sign 

and one circular 12-inch beacon located below the sign

DF Degrees of Freedom

FARS Fatality Analysis Reporting System

FHWA Federal Highway Administration

GES General Estimates System

GLMM Generalized Linear Mixed Effects Model

GPS Global Positioning System

HSIS Highway Safety Information System

IQR Interquartile Range

IRWL In-Road Warning Light

ITS Intelligent Transportation System

LED Light-Emitting Diode (also used to indicate the sign used in the closed-course 

study where the LEDs were embedded into the border)

LMM Linear Mixed Effects Model

MCOP Marketing, Communication, and Outreach Plan

MOE Measure of Effectiveness

MUTCD Manual on Uniform Traffic Control Devices

NCHRP National Cooperative Highway Research Program

NCUTCD National Committee on Uniform Traffic Control Devices

NHTSA National Highway Transportation Safety Administration

NMVCSS National Motor Vehicle Crash Sampling Survey

Ped X-ing Pedestrian Crossing (sign)

PHB Pedestrian Hybrid Beacon

Puffin Pedestrian User-Friendly Intelligent (crossing)

R-A For the closed-course study, two rectangular beacons located above the sign

R-B For the closed-course study, two rectangular beacons located below the sign

(format currently being used for the RRFB device)

REML Restricted Maximum Likelihood.

RRFB Rectangular Rapid-Flashing Beacon

SAE Society of Automotive Engineers

Std. error Standard Error of Value

TAP Technical Advisory Panel
xviii

TCRP Transit Cooperative Research Program

TFHRC Turner-Fairbank Highway Research Center

TTI Texas A&M Transportation Institute

TWLTL Two-Way Left-Turn Lane

TxDOT Texas Department of Transportation

UMKC University of Missouri—Kansas City

WO-B For the closed-course study, diamond-shaped sign with no beacons or LEDs

YTPCD Yield-to-Pedestrian Channelizing Device
1

CHAPTER 1. INTRODUCTION

BACKGROUND 

Several methods have been used to emphasize the presence of a pedestrian crossing, including 

methods with beacons or embedded light-emitting diodes (LED). A device that has received 

national attention is the rectangular rapid-flashing beacon (RRFB). On July 16, 2008, the Federal 

Highway Administration (FHWA) provided interim approval (formally listed as IA-11) for the 

optional use of rectangular rapid-flashing beacons.(1) FHWA approved the use of this device at 

pedestrian and school crosswalks across uncontrolled approaches and defined it in IA-11 as the 

following:

An RRFB shall consist of two rapidly and alternately flashing rectangular yellow 

indications having LED-array based pulsing light sources, and shall be designed, 

located, and operated in accordance with the detailed requirements specified” in the 

interim approval.(1)

RRFBs appear to be different than previously used pedestrian crossing treatments, displaying

noteworthy characteristics that seem to produce improved vehicle stopping and yielding behavior 

to pedestrians. These noteworthy characteristics include the following:

• Brighter. The use of LEDs appears to provide greater brightness, which could 

improve recognition.

• Rapid-flashing pattern. Compared with slower flashing beacons, the rapid flash 

appears to imply urgency to drivers.

• Only flashing when pedestrian is present. The treatment is activated by a 

pedestrian, typically via a pedestrian pushbutton, which results in the beacons only 

being active when a crossing is desired rather than flashing continuously. The 

activated nature of the treatment better communicates that a pedestrian is actually 

present and wanting to cross the roadway.

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

Devices (NCUTCD) assists in developing language for chapter 4 of the Manual on Uniform 

Traffic Control Devices (MUTCD).(2) The STC is interested in research and/or assistance in

developing material on the RRFB. Earlier research studies did not address certain issues that the 

STC believes are important in crafting language suitable for creating a uniform standard for the 

MUTCD. The STC sought advice on several issues, including whether the housings have to be 

rectangular and whether circular-shaped housings will achieve the same effect.

As a result of FHWA providing interim approval (as IA-11) for the optional use of RRFBs and 

the NCUTCD’s interest in addressing issues such as beacon shape and size, this research 

investigated size and shape of the beacon, brightness/glare issues, and position of the beacon. 

The research later focused on the shape (rectangular and circular) of rapid-flashing beacons. The 

research included a closed-course study and an open-road study.
2

The closed-course study was designed to investigate the ability of a driver to detect an object 

(box, trash can, or pedestrian) placed about 3 ft beyond a beacon assembly for different 

combinations of beacon shape (circular or rectangular), size (8 or 12 inches in diameter for the 

circular), and placement (above or below the sign). One of the objectives of the closed-course 

research effort was to identify assemblies for field (open road) evaluation. Because of the 

common use of the RRFB below the sign, it was considered the baseline device and was selected 

for inclusion in the open-road study. Considering the challenges with switching devices at a 

location, only one additional device was suggested for testing. The suggested alternative was the 

12-inch circular beacons located below the sign. It had longer sign legibility distances during the 

night, and longer object detection distances during both day and night compared with other 

assemblies. 

The open-road study was designed to investigate 1) whether drivers yield differently to circular 

or rectangular beacons when used with a rapid-flashing pattern, 2) whether a driver is more 

likely to yield to a pedestrian when the rapid-flashing beacon is activated than when it is not 

activated, and 3) whether vehicle traffic volume affects driver yielding.

STUDY OBJECTIVE

The research included a closed-course study and an open-road study. The closed-course study 

had the following objectives:

• Determine whether the shape, size, and placement of flashing beacon/LEDs affect the

following:

o Sign legibility and symbol identification distances.

o Object detection.

• Determine driver ratings of disability glare for 8-inch circular beacons and LEDembedded signs using a rapid flash pattern.

• Identify up to two assemblies for field evaluation to be conducted following the 

conclusion of the closed-course tasks.

The open-road study had the following objectives:

• Determine whether drivers yielded differently to circular or rectangular beacons when 

used with a rapid-flashing pattern.

• Determine to what extent, if any, a driver is more likely to yield to a pedestrian when 

the rapid-flashing beacon is activated than when it is not activated.

• Determine whether vehicle traffic volume affects driver yielding.
3

SCOPE OF WORK 

The goal of this research effort is to improve pedestrian safety at urban and suburban crossing 

locations by identifying and evaluating low- to medium-cost pedestrian countermeasures to 

reduce pedestrian fatalities and injuries at these locations. This research effort focuses on 

countermeasures that can be implemented at unsignalized pedestrian crossing locations and that 

previously have not been rigorously evaluated from a safety perspective.

STUDY APPROACH

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

Task 1—Hold Kickoff Meeting. The research team met with FHWA staff to discuss the project 

direction, scope, and work plan.

Task 2—Organize a Technical Advisory Panel. The research team identified and organized a 

Technical Advisory Panel (TAP) comprising a diverse set of stakeholders representing Federal, 

State, and local governments; academia; nonprofit organizations; and private industry. The role 

of the TAP has been to participate in meetings, provide input to FHWA and the research team, 

and review documents for the project.

Task 3.1—Conduct a Literature Review on Midblock Crossings. The research team 

conducted a literature review of pedestrian safety and behavior at midblock crossings in urban 

and suburban settings. This literature review focused on midblock crossing locations in urban 

and suburban conditions, but also included literature on pedestrian crossings at intersections.

Task 3.2—Gather Data on Pedestrian Crashes and Local Field Observations. The research 

team conducted an analysis of pedestrian crash datasets and field observations at selected 

midblock pedestrian crossing locations. The purpose of the crash dataset review was to document 

the characteristics, circumstances, and contributing factors for crashes at midblock pedestrian 

crossings, and to assess the suitability of these databases for any safety evaluations to be 

conducted in the research. The purpose of the local field observations was to serve as a first step 

toward deciding the types of treatment and locations to be studied later in the research.

Task 3.3—Prepare a Draft Work Plan for the Proposed Evaluation. Based on the results of 

Tasks 3.1 and 3.2, the research team generated a list of five proposed crossing countermeasures 

for evaluation in the research. A final selection of countermeasures for evaluation was made 

during Task 4. For each of the potential countermeasures, the research team prepared a draft 

work plan. 

Task 3.4—Develop the Marketing Plan for the Project. The research team developed the 

Marketing, Communication, and Outreach Plan (MCOP), which summarized the most cost 

effective, high-leverage communications opportunities, strategies, and tactics for a successful 

marketing, communications, and outreach campaign explaining how practitioners can use design 

and operational information related to selected countermeasures.
4

Task 4.1—Organize, Prepare, and Conduct Briefing Meeting at the Turner-Fairbank 

Highway Research Center (TFHRC) with FHWA and TAP. The research team conducted a 

briefing meeting at TFHRC that included FHWA, TAP, and members of the research team. At 

the meeting, draft work plans and a draft MCOP were presented and discussed. During the 

meeting, advice and direction for the research team was gathered. At the briefing meeting, it was 

decided that the research team would conduct a closed-course study and an open-road study, 

focusing on the shape, size, and location of rapid-flashing beacons. The research team developed 

a final work plan for these two studies. 

Task 4.2—Conduct a Teleconference or Web Conference with FHWA and TAP. The 

research team conducted a conference call with FHWA and TAP to discuss the final work plan.

Task 5—Evaluate Selected Pedestrian Crossing Countermeasures. The research team 

conducted a closed-course study to investigate the ability of a driver to detect an object placed 

beyond a beacon assembly for different combinations of beacon shape, size, and placement. The 

research team also conducted an open-road study to investigate driver yielding as a function of 

beacon shape, beacon activation, and traffic volume.

Task 6—Develop Technical Briefs, Final Comprehensive Technical Report, and MCOP.

The research team developed the following final documents for the research:

• MCOP. 

• Technical Report.

• TechBrief.

REPORT ORGANIZATION

This report includes the following chapters:

• Chapter 1. Introduction. This chapter presents general background information 

along with the research objective and the approach used for the closed-course and 

open-road studies conducted as part of this research.

• Chapter 2. Literature Review. This chapter presents findings from the literature 

review that relates to the phase II studies along with supplemental literature reviews 

conducted as part of phase II research efforts. The full literature review is included in

the appendix. The literature review findings provided direction regarding the 

countermeasures to be studied.

• Chapter 3. Gather Data on Pedestrian Crashes. This chapter documents the results 

of a review and analysis of existing pedestrian crash databases.

• Chapter 4. Local Field Observations. This chapter documents the results of field 

observations at midblock pedestrian crossings.

• Chapter 5. Closed-Course Study. This chapter documents the results of a closedcourse study designed to investigate the ability of a driver to detect an object placed 
5

beyond a beacon assembly for different combinations of beacon shape, size, and

placement.

• Chapter 6. Open-Road Study. This chapter documents the results of an open-road 

study to investigate driver yielding as a function of beacon shape, beacon activation, 

and traffic volume.

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

final chapter provides a summary and the conclusions of the research, and presents

future research needs.

• Appendix. Literature Review. The appendix contains the full literature review 

conducted during phase I.

7

CHAPTER 2. LITERATURE REVIEW

Efforts during the initial phase of this project included a comprehensive literature review of 

pedestrian treatments being used at unsignalized pedestrian crossings. The appendix to the report

contains the literature review. Certain parts of the literature review were updated or additional 

literature reviews were conducted as needed to support work done in later tasks of this project. 

The following sections contain those updated materials.

PREVIOUS OPEN-ROAD RESEARCH ON RECTANGULAR RAPID-FLASHING 

BEACONS

The RRFB flashes in an eye-catching sequence to draw drivers’ attention to the sign and the need 

to yield to a waiting pedestrian. It is located on the side of the road below pedestrian crosswalk 

or school crossing signs or overhead with a sign, and can be activated by a pedestrian either 

actively (pushing a button) or passively (detected by sensors). 

Original FHWA Study on RRFB

An FHWA study evaluated RRFBs at 22 sites in St. Petersburg, FL; Washington, DC; and 

Mundelein, IL.

(3) The RRFBs produced an increase in yielding behavior at all locations. During 

the baseline period before the introduction of the RRFB, yielding for individual sites ranged 

between 0 and 26 percent. The average yielding for all sites was 4 percent before installation of 

the RRFBs. Within 7 to 30 days following installation of an RRFB, the average yielding 

increased to 78 percent from the baseline condition, a statistically significant increase. Similar 

yielding values were observed during the remainder of the study period. 

Data collected over a 2-year period, at 18 of the sites, confirmed that the RRFBs continue to be 

effective in encouraging drivers to yield to pedestrians, even over the longer term. By the end of 

the 2-year follow-up period, the researchers determined that the introduction of the RRFB was 

associated with yielding that ranged between 72 and 96 percent. 

During the baseline measurement phase, the researchers installed advance yield markings to 

reduce the risk of multiple-threat crashes, which occur when a driver stopping to let a pedestrian 

cross is too close to the crosswalk, masking the pedestrian from drivers in the adjacent lane. The 

advance yield markings, which are recommended with an RRFB installation, were typically 

placed 30 ft in advance of the crosswalk unless a driveway or other issue was present, in which 

case they could be placed up to 50 ft in advance of the crosswalk. The posted speed limit at the 

sites ranged from 30 to 40 mi/h.

The observers scored the percentage of drivers yielding and not yielding to pedestrians. 

Researchers scored drivers as yielding if they stopped or slowed and allowed the pedestrian to 

cross. Conversely, researchers scored drivers as not yielding if they passed in front of the 

pedestrian but would have been able to stop when the pedestrian arrived at the crosswalk. 
8

2009 FHWA Study

A 2009 FHWA report presented the results of an evaluation of RRFBs at two sites in Miami, 

FL.

(4) The study team used the following measures of effectiveness (MOE) to assess the effect of 

the RRFB on pedestrian and driver behavior: 1) the percentage of pedestrians trapped in the 

roadway, 2) the percentage of drivers yielding to pedestrians, and 3) the percentage of 

pedestrian-vehicle conflicts. The researchers found statistically significant improvements in all 

of the MOEs as shown in table 1. For percent drivers yielding, only 4.2 or 4.1 percent of the 

drivers yielded at the two sites in the before condition. After the installation of the RRFB, 

yielding at the two sites increased to either 55.2-percent driver yielding or 60.1-percent driver 

yielding, depending on the site. The researchers concluded that the RRFB offered clear safety 

benefits, and it was placed into the category of highly effective countermeasures.

Table 1. Measures of effectiveness for RRFBs with pedestrian crossing signs, Miami, FL

(compiled from reference 4).

Measure of effectiveness Site Before After p-value

Percent drivers yielding 

(staged crossings, daytime)

NW 67th and Main Street 4.2 

(n=2,330)

55.2 

(n=2,131)

0.01 (daytime and nighttime 

combined at this site)

S. Bayshore and Darwin 4.1 

(n=2,075)

60.1 

(n=1,361)

0.01 (daytime and nighttime 

combined at this site)

Percent drivers yielding 

(staged crossings, 

nighttime)

NW 67th and Main Street 4.4

(n=703)

69.8 

(n=223)

0.01 (daytime and nighttime 

combined at this site)

S. Bayshore and Darwin 2.5

(n=139)

66.0 

(n=225)

0.01 (daytime and nighttime 

combined at this site)

Percent drivers yielding 

(resident crossings)

NW 67th and Main Street 12.5 

(n=137)

73.7 

(n=259) 0.001

S. Bayshore and Darwin 5.4

(n=200)

83.4 

(n=111) 0.001

Percent of pedestrians 

trapped in roadway NW 67th and Main Street 44 0.5 < 0.01

Percent of vehiclepedestrian conflicts

NW 67th and Main Street 11 2.5 < 0.05

S. Bayshore and Darwin 5.5 0 < 0.01

2009 Florida Study

A 2009 report summarized the effects of installing a pedestrian-activated RRFB at the location of 

one uncontrolled trail crossing at a busy (15,000 average daily traffic (ADT)), four-lane urban 

street in St. Petersburg, FL.

(5) The researchers used a mounted video camera to collect pre- and 

post-treatment data about pedestrian and driver interactions at the trail crossing. An analysis of 

the data showed a statistically significant increase in driver yielding (from 2 percent pretreatment 

to 35 percent post-treatment and 54 percent when the beacon was activated) and ability of 

pedestrians to cross the entire intersection (from 82 percent pretreatment to 94 percent posttreatment).

2011 Texas Study

A 2011 before-and-after study looked at the effectiveness of RRFBs at an uncontrolled crossing 

in Garland, TX.

(6) The school crosswalk on a five-lane arterial had continental crosswalk 

markings, supplemented by school crossing signs on either side of the roadway. Before
9

installation, city engineers had observed driver compliance with the crosswalk was poor and 

planned to install overhead and side-mounted RRFBs to improve compliance and facilitate 

pedestrian crossing maneuvers. In this study, researchers observed drivers’ yielding behavior for 

crossing-guard-controlled crossings and staged pedestrian crossings, both before and after 

installation of the RRFBs (see figure 1). Researchers found that while yielding to school-related 

crossings with a crossing guard remained fairly constant (with yielding rates about 90 percent), 

drivers’ responses to staged crossings in non-school-zone time periods improved from 1 percent 

before the installation to 80 percent after installation.

Source: Texas A&M Transportation Institute.

Figure 1. Photo. School crosswalk with RRFBs in Garland, TX.

(6)

2011 Oregon Study

A 2011 Oregon Department of Transportation report evaluated RRFB installation at three

crosswalks in Bend, OR.

(7) For two of the locations, the highway has a 45-mi/h posted speed 

limit and is a four-lane roadway with a center median, bike lanes, and sidewalks. Because the 

posted speed limit of 45 mi/h was greater than most locations where RRFBs have been installed 

in Oregon, the plans for the RRFB installations included additional features to increase the 

visibility of the crosswalks and the pedestrians and bicyclists using them. These include RRFB 

assemblies at three locations: on the side of the road, on the median at the crosswalk, and 500 ft 

in advance of the crosswalk. Pavement markings included ladder bars with a continental 

crosswalk, a stop line 50 ft in advance of the crosswalk, and double white solid no-lane-change 

lines, as well as the legend “PED X-ING” on the road as vehicles approach the intersection. The 

signs in the RRFB assembly were 48 inches, and there was a sign in advance of the crosswalk 

with the legend “Stop Here for Pedestrians.” Before the installation of the RRFBs, motorist yield 

rates were 23 and 25 percent at the 45-mi/h intersections and 6 percent at the third crossing. 

These rates increased to between 74 and 83 percent following treatment. The researchers

concluded that “RRFBs should be considered for installation on high-speed facilities where there 

are posted speeds greater than 35 mi/h if there are pedestrians and bicyclists using the facility 

and a history of crashes or the potential for them.”(7)
10

2013 California Study

A study of two sites in Santa Monica, CA, compared the effect of an RRFB and a circular rapidflashing beacon (CRFB) on yielding behavior at two crossings.(8) The RRFB was installed at one 

site and the CRFB at the other, and after an evaluation period, they were switched and evaluated 

again. The study evaluated driver yielding rates both when the beacons were activated and when 

they were not activated. At both sites, the beacon that was installed first showed better yielding 

rates than the beacon that was installed second. At site 1, the RRFB resulted in 85-percent 

yielding when activated and the subsequent CRFB showed 63 percent yielding, while at site 2, 

the CRFB was installed first and produced 92-percent yielding compared to 80-percent yielding 

for the RRFB. In all cases, driver yielding rates were higher (between 7 and 22 percentage 

points) when the beacon was activated than when it was not. 

2013 Canada Study

A 2013 pilot project in Calgary, AB, Canada, assessed motorists yielding behavior before and 

after installation of RRFBs.(9) Overall, the installation of the RRFB improved yielding 

compliance from 83 percent to 98 percent, which was statistically significant. 

2014 Michigan Study

A series of treatments were installed at a bike trail crossing site in Michigan in a study that 

examined the effectiveness of a “gateway” in-street sign configuration with the RRFB used alone 

and in combination.(10) Because of the presence of a sharp curve, the posted speed limit was 

25 mi/h, and the site had two through lanes (one in each direction) and a center turn lane. When 

the signs were absent and the RRFB not activated, yielding averaged 20 percent. The RRFB 

alone produced an average yielding level of 69 percent. The gateway in-street sign treatment, 

which consisted of in-street signs on the lane line on both sides of the turn lane and on each side 

of the road, produced 80-percent yielding. The combination of the gateway in-street sign 

configuration and RRFB produced 85-percent yielding. The authors concluded that the data 

showed that the gateway in-street signs produced effects that were similar to the RRFB and that 

the combination of gateway in-street signs and RRFB may produce effects similar to the gateway 

in-street signs alone, which suggests that the gateway in-street signs can be more cost effective 

than the more expensive RRFB.

2014 Texas Study

A Texas Department of Transportation (TxDOT) study examined the effectiveness of the 

following traffic control devices used at pedestrian crossings: traffic control signals, pedestrian 

hybrid beacon, and RRFBs.

(11,12) The 22 RRFB sites had School Crossing signs with the RRFB. 

While there are some RRFB sites in Texas with Pedestrian Crossing signs, all sites used in the 

Texas study had School Crossing signs. The FHWA Interim Approval for the RRFB states that 

when used, two Pedestrian Crossing or School Crossing signs shall be installed at the crosswalk, 

one on the right-hand side of the roadway and one on the left-hand sign of the roadway.(1) On a 

divided highway, the left-hand side assembly should be installed on the median, if practical, 

rather than on the far left side of the highway. A later interpretation indicated that overhead 

mounting is appropriate, and that if overhead mounting is used, a minimum of only one such sign 
11

per approach is required and it should be located over the approximate center of the lanes of the 

approach.(13) In Garland, some of the sites had the School Crossing signs located over the 

roadway on a mast arm along with the roadside installation (see figure 2). The overhead 

placements were on undivided roadways such as those with four through lanes and a two-way 

left-turn lane (TWLTL) or multilane one-way roads. The side mounts were used on divided 

roadways when the second sign could be placed in the median. The City of Garland was 

concerned that the RRFB would be outside the driver’s cone of vision or it could easily be 

obscured by a truck going in the opposite direction when located on the left side of an undivided 

roadway. The medians on the divided roadways allow a left-side installation next to traffic going 

in that direction. When the median was less than 4 ft wide, the city used an overhead installation. 

Source: Fitzpatrick et al.

Figure 2. Photo. Study site from TxDOT study showing overhead RRFB installation.(11)

Figure 3 shows the plot of driver yielding versus posted speed limit, and figure 4 shows the plot 

of driver yielding to total crossing distance for the data from the Texas study.(11) These plots 

provide an overview of the findings and relationships with the use of average site yielding 

values. The data for individual crossings were used in the analysis. The modeling results for the 

RRFBs are shown in table 2. For RRFBs, posted speed limit, total crossing distance, one-way 

versus two-way traffic, and city were all significant. RRFB sites with higher posted speed limits 

were associated with higher driver yielding values (see figure 3). As shown in figure 3, the two 

Waco sites with a 30-mi/h posted speed limit have very low driver yielding (below 40 percent). 

These sites had low traffic volumes during data collection, which resulted in several crossings 

having no vehicles yielding. Even when these two sites are removed from the model, the trend of 

higher driver yielding for higher speed was still present and statistically significant. A closer 

review of the data reveals that while driver yielding is higher for the 40-mi/h sites compared with

the 35-mi/h sites, the overall difference is very small (only 1 percentage point between the two 

averages). Therefore, while there may be a statistically significant increase in driver yielding by 

speed limit, the difference is not of practical significance. 
12

Source: Fitzpatrick et al.

Figure 3. Graph. RRFB: driver yielding to posted speed limit plot from 2014 Texas 

study.(12)

Source: Fitzpatrick et al.

Figure 4. Graph. RRFB: driver yielding to total crossing distance plot from 2014 Texas 

study.(12)

The data revealed a trend of lower driver yielding rates for wider crossing distances compared 

with shorter crossing distances (see figure 4). Perhaps drivers believe that the greater distance 

between their vehicles and the pedestrian presents the opportunity to not stop for the waiting 
13

pedestrian. For example, a driver on a six-lane road has multiple lanes in which to adjust 

position, perhaps feeling that leaving a full traffic lane between the car and the crossing 

pedestrian is sufficient. 

The model shown in table 2 uses Frisco as the base city and provides odds ratios for the two 

other cities. The driver yielding rate for Waco is lower compared with Frisco (not statistically 

significant), while driver yielding is higher for Garland (statistically significant). The greater 

number of the devices in Garland may contribute to drivers being more familiar with the 

treatment, which could be contributing to the better driver yielding behavior.

Table 2. RRFB total driver yielding model results.

(12)

Coefficientsa Estimate Standard error z value Pr(>|z|) b

Reference Levelc -2.47815 1.51421 -1.637 0.10171

M.PSL 0.12585 0.03872 3.250 0.00115**

Total_CD -0.01223 0.00617 -1.982 0.04751*

M.O_T: two-way -0.64290 0.30922 -2.079 0.03761*

City: Garland 1.39867 0.34448 4.060 4.9e-05***

City: Waco -0.52276 0.61489 -0.850 0.39523 a

Column headings are defined as follows:

• Coefficients = variables included in model.

o M.PSL = posted speed limit on major roadway.

o M.O_T = one-way or two-way operations on major roadway.

o Total_CD = total crossing distance.

o City = city where RRFB is located (Frisco, Garland, or Waco).

• Estimate: natural logarithm of the ratio: Odds (coefficient level)/Odds (reference level).

• Standard error: standard error of estimate.

• z-value: Standard normal score for Estimate, given the hypothesis that the actual odds ratio equals one.

• p-value: Probability that the observed log-odds ratio is at least as extreme as Estimate, given the hypothesis that the 

actual odds ratio equals one. b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. c

Reference Level in the model has the following conditions:

• Categorical variables base value: City = Frisco and M.O_T = one-way.

• Continuous variables range: M.PSL = 30 to 45 mi/h and Total_CD = 38 to 120 ft.

DRIVER DETECTION TO OBJECTS 

Another area of investigation for this FHWA research became the detection distance to objects 

located beyond flashing beacons. 

Previous studies have investigated detection distance to objects under various conditions. In the 

1990s, a National Cooperative Highway Research Program (NCHRP) study investigated 

stopping sight distance issues, including examining driver capabilities in detecting objects in the 

roadway.(14) Using the closed course at Texas A&M University, the researchers had 20 drivers 

detect 7 objects during nighttime conditions. Drivers indicated when they could detect and 

recognize the objects. With low-beam headlamp illumination, the researchers found that the rear 

of a vehicle was detected at a range of 725 and 1,000 ft and then recognized between 550 and 

725 ft. For high-beam illumination, the recognition distances started at about 1,100 ft, and the 

detection distances extended to almost 1,800 ft. For their pedestrian, who was a mannequin 

dressed in dark clothing, recognition under low-beam headlamp illumination was about 100 ft,

and the detection distance was about 225 ft. Under high-beam headlamp illumination, the 
14

recognition distance was about 300 ft, and the detection distance reached a maxium of almost 

500 ft.

A 2012 TxDOT study also used the Texas A&M University closed course to investigate 

nighttime detection of various objects.(15) The objective of the study was to investigate whether 

very bright traffic signs in rural conditions limited sight distance beyond the sign. The study 

included observations using both low-beam and high-beam headlamp illumination. It also 

included detection tasks without a sign present and with signs made of different retroreflective 

materials. The objects included a small gray wooden plaque, a pedestrian in blue medical scrubs, 

and the rear of a parked car. Each of the objects was placed outside the travel lane within 3.2 ft 

of the right edge line pavement marking. For the data without signs, the detection distance to the 

pedestrian was about 380 ft with low beams and 550 ft with high beams.

Comparing these two studies, the vehicle detection results look quite similar, whereas the 

pedestrian detection distances from the TxDOT study are slightly longer than those found in 

NCHRP Report 400. There are two likely causes. One is that the pedestrian used for the NCHRP 

Report 400 work was described as wearing dark clothing, while blue medical scrubs, which are 

not as dark, were used in the TxDOT 2012 study. Another reason could be the evolution of 

headlamp technologies. The NCHRP Report 400 work was conducted with a vehicle with sealed 

beam headlamps, while the TxDOT work completed with modern-day tungsten-halogen 

headlamps. Also confounding the results is the fact that the participants in the 2012 TxDOT 

study were drivers while in the NCHRP 400 study they were observers in the front seat.

A study done in 2011 looked at the impact of color contrast in the detection and recognition of 

objects in a road environment.(16) The investigation compared the nighttime object detection 

distance to several objects under three lighting systems: two LED systems with differing color 

temperatures and a fluorescent system. The objects included blue-clothed and black-clothed 

pedestrians. The results showed that the LED lighting types are significant in terms of the 

average distance at which a driver can identify a pedestrian and provide a longer detection 

distance than the fluorescent lighting. The mean detection distance was about 475 ft with the 

fluorescent system and about 600 to 675 ft for the LED systems.

PREVIOUS STUDIES ON LEGIBILITY DISTANCE FOR SYMBOL SIGNS

There is extensive research into how various characteristics of road signs such as sign size, letter

size, contrast, luminance, conspicuity, and others affect sign legibility distance. (See, for 

example, references 17, 18, 19, and 20.) Legibility distance is the location upstream of a sign 

where a driver can correctly read all of the words on a sign or correctly identify the symbol on a 

symbol sign. Examples of recent research on road sign symbol recognition are summarized in the 

following paragraphs. 

Paniati conducted a laboratory experiment to determine the relative legibility distance and driver 

comprehension of 22 symbol warning signs that were in use in the United States in the 1980s.(21)

The results showed that legibility distance decreases with participant age and that bold symbols 

of simple design provide the greatest legibility distance for all age groups. Data were collected 

using a zoom lens on a slide projector. The participants were presented with a randomly selected 

slide beginning at a simulated distance of 1,000 ft and moving equivalent to a driving speed of 
15

32 mi/h. The participant pressed a button when he or she could identify the symbol on the 

projected sign. This study included 16 participants under 45 years old and 16 participants over 

55 years old. Paniati noted that, as expected, there was a significant difference between the 

legibility distances for many of the symbols. The results indicated signs with color cues (e.g., 

signal, stop, yield ahead) or of simple design (e.g., crossroad, right turn) provided the greatest

legibility distance. As increased complexity is added to the symbol (e.g., added lane, winding 

road, reverse curve) the legibility decreases. As the details of the image to be resolved become 

finer (e.g., slippery when wet, narrow bridge, pavement ends) or the long-distance appearance of 

the images begin to resemble one another (e.g., pedestrian, worker, school crossing), the 

legibility distance continues to decrease.

A study conducted in the early 2000s had participants walk toward a calibrated, fixed-size sign 

projected on a large projection screen.(22) Two levels of performance were assessed: maximum 

recognition distance (threshold) and the distance at which the symbol types could be recognized

with ease (confident). A total of 40 subjects, half of whom were young and half older, 

participated in the study. The traffic sign background luminance, luminance contrast, and symbol 

type were found to be statistically significant in affecting the symbol recognition distance.

Observer age and background complexity were statistically nonsignificant.

Zwahlen and Schnell conducted an exploratory daytime and nighttime sign recognition and 

legibility field driving experiment involving 11 signs and 10 subjects.(23) The instructions 

emphasized that the subjects were to say aloud the information on the traffic sign at the point 

during their approach when they could clearly (with near 100-percent certainty) identify all

visual details of the message in the symbol. The average daytime legibility and recognition 

distances were about 1.8 times longer than the average nighttime legibility and recognition 

distances. One of the signs tested was the Curve Arrow (black paint on yellow engineer grade 

background) which had an average legibility/recognition distance of 1,045 ft in the daytime and 

743 ft at night. 

Conspicuity is the capacity of a sign to stand out or be distinguishable from its surroundings and 

thus be readily discovered.(24) For a sign to be conspicuous, the viewer must be able to 

differentiate it from the surrounding background. Variables affecting conspicuity include 

luminance, luminance contrast, and color contrast. The addition of beacons to a roadway sign 

can improve the conspicuity of a sign. Literature that specifically addresses the legibility distance 

for symbol signs when used with supplemental beacons was not identified. Therefore, this

FHWA study provides an opportunity to gain insights into the situation when beacons are used to 

supplement a roadside sign. Another unique aspect of this FHWA study is that participants were 

driving the vehicle while searching for the signs.

17

CHAPTER 3. GATHER DATA ON PEDESTRIAN CRASHES

This chapter documents the results of the review and analysis of pedestrian crash databases. The 

analysis of crash datasets had the following two key objectives:

• Review specific potential data sources to assess their suitability for safety evaluations 

that may be conducted in the research.

• Review the characteristics, circumstances, and contributing factors for crashes at 

midblock pedestrian crossings.

REVIEW OF CRASH DATABASES

A review of available crash datasets was conducted to assess which datasets were most 

promising for characterizing the attributes, circumstances, and contributing factors for crashes at 

midblock pedestrian crossings. National and State databases were considered in this assessment. 

The following crash databases were reviewed:

• National Highway Traffic Safety Administration (NHTSA) Fatality Analysis 

Reporting System (FARS).

• NHTSA General Estimates System (GES).

• NHTSA National Motor Vehicle Crash Sampling Survey (NMVCSS).

• FHWA Highway Safety Information System (HSIS) data for California, Illinois, 

Minnesota, North Carolina, Ohio, and Washington, as well as similar data for Texas.

The databases were selected for review because of their broad scope (especially the national 

databases), their reputation for quality, and their accessibility. These databases represent 

complete national or statewide datasets that are made available to researchers. Other agencies are 

often reluctant to release complete datasets.

An initial screening of the candidate data sources was performed to assess their suitability for 

analysis of midblock pedestrian crossings. This screening was conducted primarily with 

documentation rather than with the actual data, although portions of each dataset were given an 

initial review. The screening of the crash data sources addressed the following questions:

• Can crashes involving pedestrians be distinguished from other crashes?

• Can midblock pedestrian crashes be distinguished from intersection pedestrian 

crashes?

• Can crashes involving a pedestrian traveling along the road be distinguished from 

crashes involving a pedestrian crossing the road (or waiting to cross the road)?

• Can crashes involving a pedestrian in the traveled way be distinguished from crashes 

involving a pedestrian on a roadside or shoulder?
18

• Can crashes involving a pedestrian in a marked midblock crosswalk be distinguished 

from other crashes involving pedestrians in the traveled way?

• Can the type of traffic control device present at a midblock pedestrian crossing be 

determined?

• Can vehicle–vehicle crashes at midblock pedestrian crossings be distinguished from 

other vehicle–vehicle crashes?

The assessments of these questions are presented in table 3 for the national and State databases 

reviewed.

Table 3 shows that all of the crash datasets reviewed can distinguish pedestrian crashes from 

other crash types. Also, all of the crash datasets reviewed, except the NMVCSS, can distinguish 

midblock pedestrian crashes from intersection pedestrian crashes. NMVCSS was dropped from 

further consideration because of this deficiency.

The table shows that none of the databases distinguish clearly between crashes involving a 

pedestrian traveling along the road and crashes involving a pedestrian crossing the road. In other 

words, the available datasets generally lack a data element that clearly identifies the pedestrian 

action underway prior to the crash. Lack of data at this level of detail generally limits the 

application of the FHWA Pedestrian and Bicycle Crash Analysis Tool unless supplementary data 

were obtained from the review of hard-copy police crash reports.

Most datasets, other than the Washington State crash data, can distinguish crashes involving a 

pedestrian in a marked midblock crosswalk from other crashes involving pedestrians in the 

traveled way. It should be noted that only marked crosswalks at midblock locations are 

considered midblock crosswalks. There is no implied crosswalk at any midblock location unless 

markings and/or signs identify the location as a crossing. In the Washington data, midblock 

pedestrian crashes that occur at a signal, stop sign, or yield sign were presumed to occur at or 

near a crosswalk.

The table shows that only some crash datasets can identify the type of traffic control present at a 

midblock pedestrian crossing. Roadway characteristics files do not generally identify the 

locations of midblock crossings or the types of traffic control present at such crossings. 

However, in most States, the type of traffic control at a midblock crossing can be inferred from 

the traffic control information in crash data for crossings at which crashes have occurred.

Finally, the table shows that there are only limited cases in which a vehicle–vehicle collision at a 

midblock crossing (e.g., a rear-end collision with a vehicle stopped at the crossing) can be clearly 

distinguished from similar collisions at other midblock locations.

Based on the results shown in table 3, a decision was reached to proceed with analysis of all of 

the datasets tabulated, with the exception of the NMVCSS dataset.
19

Table 3. Assessment of screening questions for national and State databases.

Question

National data State data

FARS GES NMVCSS CA MN NC OH TX WA

What is the scope of the dataset? See 

note a

See 

note b

See 

note c

See 

note d

See 

note e

See 

note f

See 

note d — See note d

What years of data were reviewed? 2005 

to 

2009

2005 

to 

2009

—

2005 

to 

2008

2003 

to 

2007

2005 

to 

2008

2005 

to 

2008

2003 

to 

2009

2005 

to 

2008

Can crashes involving pedestrians be 

distinguished from other crashes? Yes Yes Yes Yes Yes Yes Yes Yes Yes

Can midblock pedestrian crashes be 

distinguished from intersection pedestrian 

crashes?

Yes — No Yes Yes Yes Yes Yes Yes

Can crashes involving a pedestrian traveling 

along the road be distinguished from 

crashes involving a pedestrian crossing the 

road?

Yesg Yes No No No Yes No No No

Can crashes involving a pedestrian in the 

traveled way be distinguished from crashes 

involving a pedestrian on a roadside or 

shoulder?

Yes No No Yes Yes Yes Yes Yes Noh

Can crashes involving a pedestrian in a 

marked midblock crosswalk be 

distinguished from other crashes involving a 

pedestrian in the traveled way?

Yes Yes No Yes Yes Yes Yes Noh Noh

Can the type of traffic control at a midblock 

pedestrian crossing be determined? — — No No Yesi Yesi Yesi No No

Can vehicle-vehicle crashes at midblock 

pedestrian crossings be distinguished from 

other vehicle-vehicle crashes?

— — No Noj Noj Noj Yesk No Nok

a

Nationwide data for fatal crashes only.

b

Broad-based sample of nationwide crashes of all severity levels from specific primary sampling units. c

A special-purpose sample of crashes of all severity levels whose locations are not identified. d

Crashes on State-maintained highways only for all severity levels. e

Includes crashes of all severity levels on nearly all roads statewide, including State-maintained and local facilities. f

Includes crashes of all severity levels the majority of the State-maintained road system; many State-maintained roads in North 

Carolina are equivalent to county roads or city streets in other States. g

Yes, in some, but not in all cases.

h

Can be presumed indirectly from traffic control devices present.

i

Yes, but only in the crash data for locations where crashes occur.

j

No, except to the extent this can be inferred from the location type.

k

Yes, to some extent.

— Unknown.

SUMMARY OF RESULTS

This section presents a summary of the results of an analysis of several national and State crash 

databases. The analysis focused on the characteristics, circumstances, and contributing factors 

for crashes at midblock pedestrian crossings.
20

NHTSA FARS

The FARS database contains data on all fatal crashes that occur on public roads in the United 

States. FARS crash data were reviewed for the years 2005 through 2009, inclusive. During this 

period, 22,892 pedestrian fatalities occurred in the United States, and 73.0 percent of the 

pedestrian fatalities occurred at midblock locations. The following statistics apply to the 

pedestrian fatalities that occurred at midblock locations:

• 1.3 percent occurred at marked midblock crosswalks.

• 30.7 percent occurred not in, but near, a marked midblock crosswalk.

• 68.0 percent occurred at locations that were not near marked midblock crosswalks.

Other highlights from the analysis of FARS data include the following:

• Most midblock pedestrian crashes occur at locations with no traffic control.

• For midblock crashes that occurred in or near a crosswalk, 18.6 percent were 

classified as “improper crossing.” However, this FARS pedestrian-related factor may 

be a bit ambiguous because at least two other categories—“failure to yield/obey” and 

“dart/run into roadway” —appear to be forms of “improper crossing.”

• The data show a general increasing trend in crash frequencies with increasing 

pedestrian age. Particularly notable is the high proportion of fatal pedestrian crashes 

for pedestrians of age 70 or older at midblock crosswalks.

• The proportion of male and female pedestrians killed at locations within midblock 

crosswalks is relatively even. However, for midblock locations near, but not within,

crosswalks, and for midblock locations away from crosswalks, more than 70 percent 

of the victims were male. This suggests greater risk-taking behavior on the part of 

male pedestrians.

• FARS data for FHWA’s 13 Pedestrian Safety Focus States and 5 Pedestrian Safety

Focus Cities were reviewed for fatal midblock pedestrian crashes. Collectively, the 

13 Pedestrian Safety Focus States experience 59 percent of total U.S. fatal pedestrian 

crashes at or near midblock crossings, while the 5 Pedestrian Safety Focus Cities 

represent 6 percent of total U.S. fatal pedestrian crashes at or near midblock 

crossings.

NHTSA GES

In a review of a nationwide sample of crash data for all crash severity levels from GES, the 

database includes 10,079 crashes involving a pedestrian from 2005 to 2009, inclusive. Nearly 

half of the pedestrian crashes occurred at midblock locations. However, only 2.5 percent of the 

midblock pedestrian crashes were explicitly identified as midblock crossing crashes.
21

Other highlights from the analysis of GES data include the following:

• The GES data, which include pedestrian crashes of all severity levels, indicate that 

midblock crossing crashes are typically less severe than pedestrian crashes elsewhere 

on the midblock roadway.

• “No control” was the selected traffic control device for 65 percent of midblock 

crosswalk crashes and 91 percent of the pedestrian crashes that occurred elsewhere on 

the midblock roadway.

• The GES variable called “pedestrian action” is similar to the FARS “pedestrianrelated” factor. For midblock crosswalk crashes, 4.9 percent were classified as 

“improper crossing,” while a substantial number of other midblock crosswalk crashes 

were classified in categories closely related to “improper crossing”: dart/run into 

roadway, inattentive, and “playing, working, sitting, lying, etc., in the roadway.”

• When crashes for a range of severity levels were included, there was a large 

proportion of crashes (more than 20 percent) for young pedestrians in the age range 

from 11 to 20 (i.e., primarily teenagers).

• As with the FARS data, there was a greater gender balance in pedestrians crossing at 

midblock pedestrian crosswalks (actually more females than males), but a 

substantially higher proportion of male pedestrians crossing at non-crosswalk 

locations.

FHWA HSIS

California

HSIS data for California include crash data only for the State highway system, consisting of 

approximately 15,520 mi of highways. Data analyzed for this report include the years from 2006 

to 2008, inclusive. During the study period, 3,944 pedestrian crashes occurred on the California 

State highway system. Nearly 70 percent of the pedestrian crashes occurred at midblock 

locations. Only 2.6 percent of the midblock crashes were classified as occurring at midblock 

crosswalks.

Other highlights from the analysis of California pedestrian crash data include the following:

• As observed in the GES data, crash severities appear to be lower for midblock 

crosswalk crashes than for other pedestrian crashes on the midblock roadway.

• Approximately 75 percent of pedestrian midblock crashes occurred on an undivided 

roadway.

• Most pedestrian crashes, including midblock crosswalk crashes, occurred at locations 

with no traffic control present.
22

Minnesota

HSIS data for Minnesota include crash data for nearly all crashes statewide, including those that 

occurred on both State-maintained and local-agency-maintained road systems. Data analyzed for 

this report include the years 2003 to 2007, inclusive. During the study period, 8,271 pedestrian 

crashes occurred in Minnesota. Approximately 29 percent of the pedestrian crashes occurred at 

midblock locations. Only 3.0 percent of the midblock crashes were classified as occurring at 

midblock crosswalks.

Other highlights from the analysis of Minnesota pedestrian crash data include the following:

• In contrast to other States, the proportion of fatal and incapacitating injury crashes is 

higher for midblock crosswalks than for other midblock locations in Minnesota, but 

the sample size for midblock crosswalk crashes is so small that this may not be a 

valid comparison.

• The data for midblock roadways include a substantial number of pedestrian crashes at 

either signals or stop signs; these crashes must either have occurred at nonintersection 

signals or stop signs (i.e., driveways) or the basic intersection versus nonintersection 

classification of the crashes is incorrect.

North Carolina

HSIS data for North Carolina include crash data for approximately 62,000 mi of the 77,000 mi of 

roadway on the State-maintained highway system. Data analyzed for this report include the years 

from 2005 to 2008, inclusive. During the study period, 3,847 pedestrian crashes occurred on the 

North Carolina State highway system. Nearly 85 percent of these pedestrian crashes occurred at 

midblock locations. Only 2.7 percent of the midblock crashes were classified as occurring at 

midblock crosswalks.

Ohio

HSIS data for Ohio include crash data only for the State highway system, consisting of 

approximately 19,500 mi of highways. Data analyzed for this report include the years from 2005 

to 2008, inclusive. During the study period, 4,127 pedestrian crashes occurred on the Ohio State 

highway system. Approximately, 45 percent of these pedestrian crashes occurred at midblock 

locations. Only 1.2 percent of the midblock crashes were classified as occurring at midblock 

crosswalks.

Texas

Data for Texas includes crash data for crashes both on and off the State highway system. Data 

analyzed for this report include the years 2003 to 2009, inclusive. During the study period, 

3,134,365 crashes were included in the Texas crash database. Of these, 39,993 (1.3 percent) were 

pedestrian crashes. Nearly 50 percent of the pedestrian crashes occurred at midblock locations. 

Only 136 crashes (0.7 percent of the midblock crashes) were classified as occurring at midblock 

crosswalks. 
23

Washington

HSIS data for Washington include crash data only for the State highway system, consisting of 

approximately 7,193 mi of highways. Data analyzed for this report include the years from 2005 

to 2008, inclusive. During the study period, 1,573 pedestrian crashes occurred on the 

Washington State highway system. Nearly 40 percent of these pedestrian crashes occurred at 

midblock locations. Only 5.0 percent of the midblock crashes were classified as occurring at 

midblock crosswalks.

RESULTS FROM ANALYSIS OF CRASH DATASETS

This section of the report presents the results of the analysis of all the crash datasets reviewed in 

the preceding section. In the tables presented in this section, unless otherwise stated, all 

percentages shown in parentheses are column percentages. Crashes classified as “unknown” for a 

given data element are not included in the percentages for that data element.

The crashes classified as “midblock crosswalk” crashes involved collisions with a pedestrian at 

or near a marked midblock crosswalk. Only the FARS dataset formally distinguishes between 

crashes for pedestrians in a crosswalk or near a crosswalk. The crashes classified as “midblock 

roadway” crashes involve collisions with a pedestrian removed from (i.e., not near) a crosswalk. 

The “midblock crosswalk” and “midblock roadway” crashes include only nonintersection 

crashes (i.e., not at or related to an intersection) that occurred in the traveled way or on the 

roadway shoulder; crashes that occurred outside the shoulder (i.e., on the roadside or on a 

sidewalk) have been excluded.

Some datasets show a substantial number of “midblock roadway” crashes that occur at signals or 

stop signs. Such crashes either represent signalized or stop-controlled driveways on the midblock 

roadway or the basic intersection vs. nonintersection categorization is incorrect.

The first two datasets analyzed are national datasets, the first a census of fatalities and the second 

a sample of crashes for all severity levels. The remaining datasets are for either the State 

highway system or a broader set of roads in individual States.

NHTSA FARS

The FARS database contains data on all fatal crashes that occur on public roads in the United 

States. These data can be queried on a number of crash, vehicle, and person-level variables that 

are commonly available in crash reports. Queries of several variables related to pedestrian 

fatalities such as pedestrian age, gender, and action; time of day and day of week; speed limit; 

number of lanes crossed; weather, lighting and road surface conditions; and presence of a median 

are shown in the following tables.

Nationwide FARS Data

Table 4 summarizes nationwide data for fatalities from the FARS dataset. The table shows that 

of the 198,708 fatalities that occurred in the United States in the years 2005 through 2009, 

inclusive, 22,892 (12.1 percent) involved pedestrians. Of those pedestrian fatalities, 16,700 

(73.0 percent) occurred at midblock locations; the remaining 27.0 percent of pedestrian fatalities 

occurred at intersections. Only 223 (1.3 percent) of the pedestrian fatalities at midblock locations 
24

occurred at marked midblock crosswalks. Another 5,129 (30.7 percent) of pedestrian fatalities at 

midblock locations occurred not in, but near, a marked midblock crosswalk. Thus, 5,352 of 

pedestrian fatalities at midblock locations occurred in or near a crosswalk. The remaining 

11,348 (68.0 percent) of pedestrian fatalities at midblock locations occurred at locations that 

were not near marked midblock crosswalks.

Table 4. Summary of FARS pedestrian fatalities data (2005–2009).

Crashes

Number of fatal crashes by year

2005 2006 2007 2008 2009 Combined

Total fatalities 43,510 42,708 41,259 37,423 33,808 198,708

Pedestrian fatalities 4,892 4,795 4,699 4,414 4,092 22,892

Pedestrian midblock fatalities—

in crosswalk 63 48 44 38 30 223

Pedestrian midblock fatalities—

near crosswalk 1,373 1,252 860 815 829 5,129

Pedestrian midblock fatalities—

total crosswalk 1,436 1,300 904 853 859 5,352

Pedestrian midblock fatalities—

no crosswalk 2,240 2,255 2,493 2,323 2,037 11,348

Pedestrian fatalities—

all midblock locations 3,676 3,555 3,397 3,176 2,896 16,700

Table 5 presents nationwide data from FARS for the type of traffic control present at the 

locations of fatal midblock pedestrian crashes. The table shows clearly that most midblock 

pedestrian crashes occur at locations with no traffic control. For locations at or near marked 

midblock crossings, 85 percent of fatal pedestrian crashes occurred at locations with no control, 

while 15 percent occurred at locations with some positive control. Clearly, “no control” in this 

database is being interpreted to mean no control other than pavement markings or signing, 

because such controls must be present at a midblock crossing. Table 5 also illustrates some other 

classification issues in the data. The fatal pedestrian crashes with a “midblock roadway” location 

presumably occurred at locations removed from midblock pedestrian crossings and intersections,

yet 2.1 percent of the crashes appear to have occurred at signals or at stop signs, including 

0.1 percent at locations with pedestrian signals. Possibly, these crashes occurred at signalized or 

stop-controlled driveways; for purposes of this analysis, these might better be classified as 

intersection crashes.

Table 6 shows nationwide data for a FARS variable called “pedestrian-related factor” that 

describes the action taken by the pedestrian(s) involved in the crash. For midblock crashes that 

occurred in or near a crosswalk, 18.6 percent were classified as “improper crossing,” while 

8.0 percent were classified as “dart/run into roadway,” and 6.5 percent were classified as “failure 

to yield/obey.” Both of the latter categories appear to be forms of “improper crossing.” Nearly 

3 percent of midblock crosswalk crashes are classified as “walk, etc., in roadway.” This category 

is ambiguous, because it could imply improper crossing or it could imply that a pedestrian was 

walking along the roadway (i.e., traveling in a longitudinal direction) in the traveled way and 

happened to be struck by a motor vehicle at the location of a midblock crosswalk. The “walk, 

etc., in roadway” crashes in the “midblock roadway” column clearly imply that the pedestrian 

was walking along the roadway.
25

Table 5. FARS midblock pedestrian fatalities by traffic control device (2005–2009).

Traffic control device

Number (percent) of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near 

crosswalk

Midblock—

crosswalk 

total

Midblock—

roadway

All midblock 

locations

No controls 114 (51.3) 4,417 (86.4) 4,531 (84.9) 10,752 (95.0) 15,283 (91.8)

Control (no pedestrian

signal) 4 (1.8) 18 (0.4) 22 (0.4) 5 (0.0) 27 (0.2)

Control (with pedestrian

signal) 19 (8.5) 42 (0.8) 61 (1.1) 11 (0.1) 72 (0.4)

Control (pedestrian

signal unknown) 39 (17.7) 111 (2.2) 150 (2.8) 116 (1.0) 266 (1.6)

Flashing control 4 (1.8) 4 (0.1) 8 (0.1) 1 (0.0) 9 (0.0)

Flashing beacon 1 (0.4) 0 (0.0) 1 (0.0) 3 (0.0) 4 (0.0)

Flashing signal 

unknown 0 (0.0) 1 (0.0) 1 (0.0) 3 (0.0) 4 (0.0)

Lane signal 0 (0.0) 10 (0.2) 10 (0.2) 1 (0.0) 11 (0.1)

Other signal 3 (1.3) 8 (0.2) 11 (0.2) 8 (0.1) 19 (0.1)

Unknown signal 3 (1.3) 4 (0.1) 7 (0.1) 7 (0.1) 14 (0.1)

Stop sign 7 (3.1) 47 (0.9) 54 (1.0) 93 (0.8) 147 (0.9)

Yield sign 5 (2.2) 1 (0.2) 6 (0.1) 6 (0.1) 12 (0.1)

Other sign 7 (3.1) 408 (8.0) 415 (7.8) 184 (1.6) 599 (3.6)

Unknown sign 3 (1.3) 1 (0.0) 4 (0.1) 11 (0.1) 15 (0.1)

School: other sign 1 (0.4) 1 (0.0) 2 (0.0) 0 (0.0) 2 (0.0)

School: unknown type 0 (0.0) 4 (0.1) 4 (0.1) 4 (0.0) 8 (0.0)

Warning sign 12 (5.4) 15 (0.3) 27 (0.5) 59 (0.5) 86 (0.5)

Electronic warning sign 0 (0.0) 0 (0.0) 0 (0.0) 11 (0.1) 11 (0.1)

Officer/crossing guard 1 (0.4) 14 (0.3) 15 (0.3) 32 (0.3) 47 (0.3)

RR: Gates 0 (0.0) 0 (0.0) 0 (0.0) 4 (0.0) 4 (0.0)

RR: Flash/lights 0 (0.0) 3 (0.1) 3 (0.1) 0 (0.0) 3 (0.0)

Grade crossing, 

unknown 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.0) 1 (0.0)

Other 0 (0.0) 3 (0.1) 3 (0.1) 8 (0.1) 11 (0.1)

Unknown 0 — 17 — 17 — 28 — 45 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

RR = Railroad.

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
26

Table 6. FARS midblock pedestrian fatalities by pedestrian actions (2005–2009).

Pedestrian-related factor

Number (percent) of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

roadway

All midblock 

locations

None, not applicable 143 (65.3) 1,767 (35.0) 1,910 (36.3) 2,928 (26.4) 4,838 (29.6)

Not visible 15 (6.8) 519 (10.3) 534 (10.1) 1,109 (10.0) 1,643 (10.0)

Dart/run into roadway 14 (6.4) 408 (8.1) 422 (8.0) 1,249 (11.3) 1,671 (10.2)

Improper crossing 25 (11.4) 954 (18.9) 979 (18.6) 1,785 (16.1) 2,764 (16.9)

Walk, etc., in roadway 6 (2.7) 888 (17.6) 894 (17.0) 2,041 (18.4) 2,935 (17.9)

Interfere driver 0 (0.0) 2 (0.0) 2 (0.0) 19 (0.2) 21 (0.1)

Blackout 0 (0.0) 7 (0.1) 7 (0.1) 19 (0.2) 26 (0.2)

Emotional 0 (0.0) 5 (0.1) 5 (0.1) 15 (0.1) 20 (0.1)

Mentally challenged 1 (0.5) 5 (0.1) 6 (0.1) 4 (0.0) 10 (0.1)

Construction worker 0 (0.0) 15 (0.3) 15 (0.3) 48 (0.4) 63 (0.4)

Inattentive 1 (0.5) 39 (0.8) 40 (0.8) 70 (0.6) 110 (0.7)

Cane/crutch 0 (0.0) 2 (0.0) 2 (0.0) 8 (0.1) 10 (0.1)

Previous injury 0 (0.0) 5 (0.1) 5 (0.1) 26 (0.2) 31 (0.2)

Influence 

drug/alcohol/medicine

4 (1.8) 60 (1.2) 64 (1.2) 243 (2.2) 307 (1.9)

Blind 0 (0.0) 1 (0.0) 1 (0.0) 6 (0.1) 7 (0.0)

Other physical 0 (0.0) 4 (0.1) 4 (0.1) 3 (0.0) 7 (0.0)

Dead fetus 0 (0.0) 0 (0.0) 0 (0.0) 3 (0.0) 3 (0.0)

Jogging 0 (0.0) 5 (0.1) 5 (0.1) 10 (0.1) 15 (0.1)

On prohibited traffic way 0 (0.0) 2 (0.0) 2 (0.0) 70 (0.6) 72 (0.4)

Failure to yield/obey 10 (4.6) 333 (6.6) 343 (6.5) 1,368 (12.3) 1,709 (10.4)

On/off moving vehicle 0 (0.0) 9 (0.2) 9 (0.2) 25 (0.2) 34 (0.2)

Non-driver flees 0 (0.0) 2 (0.0) 2 (0.0) 0 (0.0) 2 (0.0)

Weather 0 (0.0) 3 (0.1) 3 (0.1) 1 (0.0) 4 (0.0)

Parked vehicle 0 (0.0) 1 (0.0) 1 (0.0) 0 (0.0) 1 (0.0)

Other obstruction 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.0) 1 (0.0)

Emergency 0 (0.0) 0 (0.0) 0 (0.0) 6 (0.1) 6 (0.0)

Law enforcement officer 0 (0.0) 2 (0.0) 2 (0.0) 17 (0.2) 19 (0.1)

Pushed by pedestrian 0 (0.0) 5 (0.1) 5 (0.1) 25 (0.2) 30 (0.2)

Portable electronic device 0 (0.0) 0 (0.0) 0 (0.0) 2 (0.0) 2 (0.0)

Unknown 4 — 86 — 90 — 249 — 339 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 7 presents nationwide data for fatal pedestrian crashes at midblock locations by pedestrian 

age. The table shows a general increasing trend in crash frequencies with increasing pedestrian 

age. Particularly notable is the high proportion of fatal pedestrian crashes for pedestrians of age 

70 or older at midblock crosswalks. There is an unexplained trend in all columns of the table 

showing that pedestrians in the 61 to 70 age group had a lower proportion of crashes than the age 

groups on either side.

Table 8 presents nationwide data for fatal pedestrian crashes at midblock locations by pedestrian 

gender. The table shows a relatively even proportion of male and female pedestrians killed at 

locations within midblock crosswalks (56 and 44 percent). However, for midblock locations 

near, but not within crosswalks, and for midblock locations away from crosswalks, more than 

70 percent of the victims were male. This suggests greater risk-taking behavior on the part of 

male pedestrians.
27

Table 9 summarizes the fatal pedestrian crash data by the number of lanes crossed. While 

interesting, these data are not terribly meaningful without exposure data on the number of lanes 

crossed by pedestrians who were not killed in crashes. The same is true for table 10, which 

categorizes fatal pedestrian crashes by the presence of a median, and thus a pedestrian refuge 

area, on the roadway crossed and table 11, which categorizes crashes by the posted speed limit at 

the crash location. Table 12 through table 16 present nationwide FARS data for fatal pedestrian 

crashes at midblock locations classified by weather condition, road surface condition, light 

condition, hour of the day, and day of the week, respectively.

Table 7. FARS midblock pedestrian fatalities by pedestrian age (2005–2009).

Pedestrian 

age

Number (percent) of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

0 to 10 7 (3.1) 240 (4.7) 247 (4.6) 500 (4.4) 747 (4.5)

11 to 20 13 (5.8) 421 (8.2) 434 (8.1) 1,109 (9.8) 1,543 (9.2)

21 to 30 14 (6.3) 751 (14.6) 765 (14.3) 1,889 (16.6) 2,654 (15.9)

31 to 40 19 (8.5) 708 (13.8) 727 (13.6) 1,705 (15.0) 2,432 (14.6)

41 to 50 26 (11.7) 1,091 (21.3) 1,117 (20.8) 2,327 (20.6) 3,444 (20.6)

51 to 60 43 (19.3) 801 (15.6) 844 (15.8) 1,657 (14.6) 2,501 (15.0)

61 to 70 29 (13.0) 430 (8.4) 459 (8.6) 842 (7.4) 1,301 (7.8)

70+ 72 (32.3) 687 (13.4) 759 (14.2) 1,319 (11.6) 2,078 (12.4)

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

Table 8. FARS midblock pedestrian fatalities by pedestrian gender (2005–2009).

Pedestrian 

gender

Number (percent) of fatalities by pedestrian gender

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

Male 125 (56.1) 3,674 (71.7) 3,799 (71.0) 8,319 (73.4) 12,118 (72.6)

Female 98 (43.9) 1,453 (28.3) 1,551 (29.0) 3,023 (26.6) 4,574 (27.4)

Unknown 0 — 2 — 2 — 6 — 8 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
28

Table 9. FARS midblock pedestrian fatalities by number of lanes crossed (2005–2009).

Number of lanes 

crossed

Number (percent) of fatalities by number of lanes crossed

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

One lane 3 (1.4) 41 (0.8) 44 (0.8) 178 (1.6) 222 (1.4)

Two lanes 131 (60.9) 3,149 (62.3) 3,280 (62.2) 6,591 (59.4) 9,871 (60.3)

Three lanes 15 (7.0) 739 (14.6) 754 (14.3) 1,329 (12.0) 2,083 (12.7)

Four lanes 49 (22.8) 839 (16.6) 888 (16.6) 2,236 (20.1) 3,124 (19.1)

Five lanes 8 (3.7) 121 (2.4) 129 (2.4) 383 (3.4) 512 (3.1)

Six lanes 7 (3.3) 116 (2.3) 123 (2.3) 303 (2.7) 426 (2.6)

Seven or more 

lanes 2 (0.9) 50 (1.0) 52 (1.0) 87 (0.8) 139 (0.8)

Unknown 8 — 74 — 82 — 241 — 323 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 10. FARS midblock pedestrian fatalities by presence and type of median

(2005–2009).

Median type

Number (percent) of fatalities by median type

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

Not divided 142 (64.8) 2,716 (53.5) 2,858 (53.9) 5,317 (47.5) 8,157 (49.6)

Median—

no barrier 39 (17.8) 1,370 (27.0) 1,409 (26.7) 2,690 (24.0) 4,099 (24.9)

Median—

with barrier 17 (7.8) 666 (13.1) 683 (12.9) 1,961 (17.5) 2,644 (16.0)

Not divided—one 

way traffic 7 (3.2) 67 (1.3) 74 (1.4) 181 (1.6) 255 (1.5)

Not divided—two 

way left-turn lane 13 (5.9) 233 (4.6) 246 (4.6) 869 (7.8) 1,115 (6.8)

Entrance/exit 

ramp 1 (0.5) 27 (0.5) 28 (0.5) 176 (1.6) 204 (1.2)

Unknown 4 — 50 — 54 — 154 — 208 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
29

Table 11. FARS midblock pedestrian fatalities by posted speed limit (2005–2009).

Posted speed 

limit (mi/h)

Number (percent) of fatalities by posted speed limit

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

No limit 2 (0.9) 5 (0.1) 7 (0.1) 46 (0.4) 53 (0.3)

5 0 (0.0) 3 (0.1) 3 (0.1) 5 (0.0) 8 (0.0)

10 1 (0.5) 6 (0.1) 7 (0.1) 7 (0.1) 14 (0.1)

15 2 (0.9) 17 (0.3) 19 (0.4) 41 (0.4) 60 (0.4)

20 6 (2.8) 24 (0.5) 30 (0.6) 47 (0.4) 77 (0.5)

25 33 (15.6) 382 (7.8) 415 (8.1) 670 (6.1) 1,085 (6.8)

30 30 (14.2) 482 (9.9) 512 (10.0) 739 (6.7) 1,251 (7.8)

35 74 (34.9) 891 (18.2) 965 (18.9) 1,509 (13.8) 2,474 (15.4)

40 27 (12.7) 597 (12.2) 624 (12.2) 1,070 (9.8) 1,694 (10.6)

45 26 (12.3) 1,018 (20.8) 1,044 (20.6) 1,829 (16.7) 2,873 (18.0)

50 7 (3.3) 225 (4.6) 232 (4.5) 555 (5.1) 787 (4.9)

55 4 (1.9) 766 (15.7) 770 (15.1) 2,045 (18.7) 2,815 (17.5)

60 0 (0.0) 121 (2.5) 121 (2.4) 521 (4.8) 642 (4.0)

65 0 (0.0) 226 (4.6) 226 (4.4) 1,353 (12.4) 1,579 (9.8)

70 0 (0.0) 111 (2.3) 111 (2.2) 440 (4.0) 551 (3.4)

75 0 (0.0) 17 (0.3) 17 (0.3) 71 (0.6) 88 (0.5)

95 0 (0.0) 0 (0.0) 0 (0.0) 1 (0.0) 1 (0.0)

Unknown 11 — 238 — 249 — 399 — 648 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 12. Midblock pedestrian fatalities by weather condition (2005–2009).

Weather 

condition

Number (percent) of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

Clear/cloudy 205 (92.0) 4,566 (89.3) 4,771 (89.3) 10,144 (90.0) 14,915 (89.8)

Rain/sleet 13 (5.8) 454 (8.9) 467 (8.9) 865 (7.7) 1,332 (8.0)

Snow 4 (1.8) 39 (0.8) 43 (0.8) 95 (0.8) 138 (0.8)

Fog 0 (0.0) 43 (0.8) 43 (0.8) 126 (1.1) 169 (1.0)

Wind 0 (0.0) 1 (0.0) 1 (0.0) 10 (0.1) 11 (0.1)

Blow sand 0 (0.0) 0 (0.0) 0 (0.0) 2 (0.0) 2 (0.0)

Other 1 (0.4) 8 (0.2) 9 (0.2) 31 (0.3) 40 (0.2)

Unknown 0 — 18 — 18 — 75 — 93 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
30

Table 13. FARS midblock pedestrian fatalities by road surface condition (2005–2009).

Road surface 

condition

Number (percent) of fatalities by pedestrian location

Midblock—in 

crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

Dry 199 (89.3) 4,298 (84.1) 4,497 (84.3) 9,741 (86.4) 14,238 (85.7)

Wet 21 (9.4) 725 (14.2) 746 (14.0) 1,340 (11.9) 2,086 (12.6)

Snowy, icy 3 (1.3) 80 (1.6) 83 (1.6) 173 (1.5) 156 (0.9)

Sand 0 (0.0) 0 (0.0) 0 (0.0) 6 (0.1) 6 (0.0)

Water/other 0 (0.0) 9 (0.2) 9 (0.2) 10 (0.1) 19 (0.1)

Unknown 0 — 17 — 17 — 78 — 95 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 14. FARS midblock pedestrian fatalities by light condition (2005–2009).

Light 

condition

Number (percent) of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

Daylight 112 (50.2) 1,098 (21.5) 1,210 (22.7) 2,516 (22.3) 3,726 (22.5)

Dusk-dawn 8 (3.7) 218 (4.3) 226 (4.2) 400 (3.6) 626 (3.8)

Dark-street 

lights 71 (31.8) 1,840 (36.0) 1,983 (37.2) 4,762 (42.3) 6,745 (40.7)

Dark-no street 

lights 32 (14.3) 1,951 (38.2) 1,911 (35.9) 3,582 (31.8) 5,493 (33.1)

Unknown 0 — 22 — 22 — 88 — 110 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
31

Table 15. FARS midblock pedestrian fatalities by hour of day (2005–2009).

Hour of day

Number (percent) of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

12–12:59 a.m. 3 (1.3) 213 (4.2) 216 (4.1) 521 (4.6) 737 (4.4)

1–1:59 a.m. 7 (3.1) 242 (4.8) 249 (4.7) 541 (4.8) 790 (4.8)

2–2:59 a.m. 3 (1.3) 215 (4.2) 218 (4.1) 630 (5.6) 848 (5.1)

3–3:59 a.m. 0 (0.0) 168 (3.3) 168 (3.2) 420 (3.7) 588 (3.5)

4–4:59 a.m. 1 (0.4) 144 (2.8) 145 (2.7) 342 (3.0) 487 (2.9)

5–5:59 a.m. 2 (0.9) 167 (3.3) 169 (3.2) 431 (3.8) 600 (3.6)

6–6:59 a.m. 12 (5.4) 220 (4.3) 232 (4.4) 436 (3.9) 668 (4.0)

7–7:59 a.m. 13 (5.9) 111 (2.2) 124 (2.3) 223 (2.0) 347 (2.1)

8–8:59 a.m. 19 (8.5) 81 (1.6) 100 (1.9) 153 (1.4) 253 (1.5)

9–9:59 a.m. 9 (4.0) 73 (1.4) 82 (1.5) 156 (1.4) 238 (1.4)

10–10:59 a.m. 13 (5.8) 77 (1.5) 90 (1.7) 149 (1.3) 239 (1.4)

11–11:59 a.m. 7 (3.1) 83 (1.6) 90 (1.7) 165 (1.5) 255 (1.5)

Noon–12:59 p.m. 6 (2.7) 82 (1.6) 88 (1.7) 207 (1.8) 295 (1.8)

1–1:59 p.m. 2 (0.9) 72 (1.4) 74 (1.4) 208 (1.8) 282 (1.7)

2–2:59 p.m. 7 (3.1) 82 (1.6) 89 (1.7) 224 (2.0) 313 (1.9)

3–3:59 p.m. 7 (3.1) 110 (2.2) 117 (2.2) 242 (2.1) 359 (2.2)

4–4:59 p.m. 11 (4.9) 126 (2.5) 137 (2.6) 274 (2.4) 411 (2.5)

5–5:59 p.m. 20 (9.1) 263 (5.2) 283 (5.3) 521 (4.6) 804 (4.8)

6–6:59 p.m. 20 (9.1) 420 (8.2) 440 (8.3) 848 (7.5) 1,288 (7.8)

7–7:59 p.m. 15 (6.7) 427 (8.4) 442 (8.3) 954 (8.5) 1,396 (8.4)

8–8:59 p.m. 11 (4.9) 503 (9.9) 514 (9.7) 1017 (9.0) 1,531 (9.2)

9–9:59 p.m. 16 (7.3) 515 (10.1) 531 (10.0) 1075 (9.5) 1,606 (9.7)

10–10:59 p.m. 10 (4.5) 401 (7.9) 411 (7.7) 857 (7.6) 1,268 (7.6)

11–11:59 p.m. 9 (4.0) 299 (5.9) 308 (5.8) 682 (6.0) 990 (6.0)

Unknown 0 — 35 — 35 — 72 — 107 —

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 16. FARS midblock pedestrian fatalities by day of week (2005–2009).

Day of week

Number (percent) of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All midblock 

locations

Sunday 33 (14.8) 729 (14.2) 762 (14.2) 1,764 (15.5) 2,526 (15.1)

Monday 25 (11.2) 636 (12.4) 661 (12.4) 1,385 (12.2) 2,046 (12.3)

Tuesday 32 (14.3) 637 (12.4) 669 (12.5) 1,414 (12.5) 2,083 (12.5)

Wednesday 35 (15.7) 676 (13.2) 711 (13.3) 1,487 (13.1) 2,198 (13.2)

Thursday 33 (14.8) 658 (12.8) 691 (12.9) 1,406 (12.4) 2,097 (12.6)

Friday 36 (16.2) 827 (16.1) 863 (16.1) 1,821 (16.0) 2,684 (16.0)

Saturday 29 (13.0) 966 (18.9) 995 (18.6) 2,071 (18.3) 3,066 (18.3)

Total 223 (100.0) 5,129 (100.0) 5,352 (100.0) 11,348 (100.0) 16,700 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
32

FARS Data by State

Table 17 presents the frequencies of fatal pedestrian crashes at midblock locations by State. 

Clearly the fatal crash frequencies are strongly influenced by State population; however, the 

extent of walking activity and the pedestrian age distribution also likely have an important role. 

For example, Florida has the highest frequency of fatal pedestrian crashes at midblock crosswalk 

locations in the United States and nearly as high a frequency of total midblock crashes as 

California, the most populous State.

The data in table 17 also suggest inconsistencies in the classification of midblock “in crosswalk” 

versus “near crosswalk” crashes, because the frequency of crashes in midblock crosswalks in 

many States is so small.

Table 17. FARS midblock pedestrian fatalities by State (2005–2009).

State

Number (percent) of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All 

midblock 

locations

Alabama 1 146 147 156 303

Alaska 0 14 14 10 24

Arizona 9 36 45 440 485

Arkansas 0 30 30 116 146

California 53 241 294 2,016 2,310

Colorado 3 25 28 147 175

Connecticut 2 2 4 120 124

Delaware 1 9 10 58 68

District of Columbia 0 34 34 6 40

Florida 7 1,262 1,269 691 1,960

Georgia 26 450 476 173 649

Hawaii 8 20 28 41 69

Idaho 0 29 29 10 39

Illinois 2 94 96 372 468

Indiana 0 196 196 19 215

Iowa 1 5 6 73 79

Kansas 1 52 53 25 78

Kentucky 0 33 33 170 203

Louisiana 1 39 40 384 424

Maine 1 17 18 17 35

Maryland 4 377 381 42 423

Massachusetts 16 65 81 121 202

Michigan 2 3 5 492 497

Minnesota 1 42 43 57 100

Mississippi 0 7 7 264 271

Missouri 1 151 152 163 315

Montana 0 22 22 20 42

Nebraska 1 17 18 8 26

Nevada 10 84 94 95 189

New Hampshire 2 14 16 8 24

New Jersey 4 92 96 303 399

New Mexico 4 81 85 119 204

New York 9 289 298 438 736

North Carolina 2 49 51 688 739
33

State

Number (percent) of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All 

midblock 

locations

North Dakota 1 10 11 11 22

Ohio 7 343 350 24 374

Oklahoma 3 8 11 198 209

Oregon 8 68 76 85 161

Pennsylvania 10 238 248 279 527

Rhode Island 1 14 15 25 40

South Carolina 1 45 46 460 506

South Dakota 0 9 9 23 32

Tennessee 1 70 71 180 251

Texas 7 80 87 1,603 1,690

Utah 4 25 29 45 74

Vermont 0 0 0 5 5

Virginia 2 43 45 232 277

Washington 3 97 100 96 196

West Virginia 0 22 22 66 88

Wisconsin 3 25 28 139 167

Wyoming 0 5 5 15 20

Total 223 5,129 5,352 11,348 16,700

FARS Data for FHWA Pedestrian Safety Focus States and Cities

Table 18 summarizes the FARS data for fatal midblock pedestrian crashes in FHWA’s 

13 Pedestrian Safety Focus States and 5 Pedestrian Safety Focus Cities, because they were 

designated during the period reported (2005–2009). Collectively, the 13 Pedestrian Safety Focus 

States experience 59 percent of the total U.S. fatal pedestrian crashes at or near midblock 

crossings, while the 5 Pedestrian Safety Focus Cities represent 6 percent of total U.S. fatal 

pedestrian crashes at or near midblock crossings.
34

Table 18. FARS midblock pedestrian fatalities for FHWA pedestrian safety focus States 

and cities (2005–2009).

State/city

Number of fatalities by pedestrian location

Midblock—

in crosswalk

Midblock—

near crosswalk

Midblock—

crosswalk total

Midblock—

no crosswalk

All 

midblock 

locations

Focus States

Arizona 9 36 45 440 485

California 53 241 294 2,016 2,310

Florida 7 1,262 1,269 691 1,960

Georgia 26 450 476 173 649

Hawaii 8 20 28 41 69

Illinois 2 94 96 372 468

Nevada 10 84 94 95 189

New Jersey 4 92 96 303 399

New Mexico 4 81 85 119 204

New York 9 289 298 438 736

North Carolina 2 49 51 688 739

Pennsylvania 10 238 248 279 527

Texas 7 80 87 1,603 1,690

Focus Cities

Chicago, IL 1 40 41 119 160

Los Angeles, CA 7 51 58 212 270

New York, NY 3 130 133 135 268

Phoenix, AZ 6 24 30 146 176

Washington, DC 0 39 39 15 54

NHTSA GES

GES is a database of a statistical sample of crashes from across the United States. These data are 

gathered in established primary sampling units (i.e., selected geographic areas) throughout the 

United States. The crash frequencies available in the GES database are shown in this section. 

The crash frequencies have not been inflated by sampling weights.

Table 19 summarizes the nationwide sample of crash data for all crash severity levels from GES. 

The database includes 680,316 crashes in the period from 2005 through 2009, inclusive. Of these 

crashes, 10,079 (1.5 percent) involved a pedestrian. Nearly half of the pedestrian crashes 

(46.7 percent) occurred at midblock locations, while the rest occurred at intersections. Only 

127 (2.5 percent) of the 4,707 midblock pedestrian crashes were explicitly identified as midblock 

crossing crashes.

Because GES includes pedestrian crashes of all severity levels, table 20 presents the distribution 

of midblock pedestrian crashes by crash severity level. The table shows that midblock crossing 

crashes are typically less severe than pedestrian crashes elsewhere on the midblock roadway.

Table 21 presents GES data for pedestrian crashes by the type of traffic control device present. 

There was no control present for 65 percent of midblock crosswalk crashes and 91 percent of 

pedestrian crashes elsewhere on the midblock roadway. As in the FARS data, a small percentage 

of “midblock roadway” crashes are shown as having traffic signals, stop signs, or yield signs 

present. This may represent traffic control at driveways.
35

Table 19. Summary of GES pedestrian crash data (2005–2009).

Crashes

Number of crashes by year

2005 2006 2007 2008 2009 Combined

Total crashes 137,884 141,412 152,727 137,303 110,990 680,316

Pedestrian crashes 1,778 2,007 2,356 2,160 1,778 10,079

Pedestrian midblock crashes 796 892 1,186 1,015 818 4,707

Midblock roadway crashes 780 896 1,153 987 791 4,580

Pedestrian midblock crossing crashes 16 23 33 28 27 127

Table 20. GES midblock pedestrian crashes by injury severity (2005–2008).

Injury severity

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Fatal 5 (3.9) 342 (7.5) 347 (7.4)

Incapacitating injury 35 (27.6) 1,475 (32.3) 1,510 (32.1)

Nonincapacitating injury 78 (61.4) 2,451 (53.6) 2,529 (53.8)

Possible injury 7 (5.5) 237 (5.2) 244 (5.2)

Injury, severity unknown 1 (0.8) 44 (1.0) 45 (1.0)

Property damage only 1 (0.8) 22 (0.5) 23 (0.5)

Unknown 0 — 9 — 9 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 21. GES midblock pedestrian crashes by traffic control device (2005–2009).

Traffic control device

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

No controls 78 (65.0) 4,016 (90.7) 4,094 (90.0)

Traffic signal 6 (5.0) 140 (3.2) 146 (3.2)

Flashing signal/beacon 3 (2.5) 5 (0.1) 8 (0.2)

Other signal 6 (5.0) 0 (0.0) 6 (0.1)

Unknown signal 1 (0.8) 0 (0.0) 1 (0.0)

Stop sign 10 (8.3) 71 (1.6) 81 (1.8)

Yield sign 1 (0.8) 3 (0.1) 4 (0.1)

School zone sign 0 (0.0) 11 (0.2) 11 (0.2)

Other sign 3 (2.5) 10 (0.2) 13 (0.3)

Unknown sign 0 (0.0) 1 (0.0) 1 (0.0)

Advisory speed sign 5 (4.2) 84 (1.9) 89 (2.0)

Warning sign for construction 0 (0.0) 10 (0.2) 10 (0.2)

Officer/crossing guard 1 (0.8) 36 (0.8) 37 (0.8)

Active device at RR crossing 0 (0.0) 1 (0.0) 1 (0.0)

Traffic control present—no details 3 (2.5) 4 (0.1) 7 (0.2)

Other 3 (2.5) 38 (0.9) 41 (0.9)

Unknown 7 — 150 — 157 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 22 shows data for the GES variable called “pedestrian action,” which is similar to the 

FARS “pedestrian-related” factor. For midblock crosswalk crashes, 4.9 percent were classified as 

“improper crossing,” while a substantial number of other midblock crosswalk crashes were 

classified in categories closely related to “improper crossing”: dart/run into roadway, 

13.1 percent; inattentive, 2.5 percent; and “playing, working, sitting, lying, etc., in the roadway,” 
36

0.8 percent. The categories related to “improper crossing” taken together constitute 21.3 percent 

of midblock crosswalk crashes. 

Table 22. GES midblock pedestrian crashes by pedestrian action (2005–2009).

Pedestrian action

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

No action 87 (71.3) 697 (15.8) 784 (17.3)

Dart/run into roadway 16 (13.1) 1,265 (28.7) 1,281 (28.3)

Improper crossing 6 (4.9) 1,222 (27.8) 1,228 (27.1)

Inattentive 3 (2.5) 33 (0.8) 36 (0.8)

Jogging 0 (0.0) 14 (0.3) 14 (0.3)

Pushing a vehicle 0 (0.0) 10 (0.2) 10 (0.2)

Walking with traffic 0 (0.0) 244 (5.5) 244 (5.4)

Walking against traffic 0 (0.0) 71 (1.6) 71 (1.6)

Playing, working, sitting, 

lying, etc, in roadway 1 (0.8) 508 (11.5) 509 (11.2)

Other 9 (7.4) 339 (7.7) 348 (7.7)

Unknown 5 — 177 — 182 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 23 presents the distribution of midblock pedestrian crashes by pedestrian age. When 

crashes for a range of severity levels are included, there is a large proportion of crashes (more 

than 20 percent) for young pedestrians in the age range from 11 to 20 (i.e., primarily teenagers). 

In addition, as with the FARS data, there are relatively fewer crashes in the pedestrian age group 

from age 61 to 70 than in the age ranges on either side.

Table 23. GES midblock pedestrian crashes by pedestrian age (2005–2009).

Pedestrian age

Number (percent) of crashes by pedestrian age

Midblock crosswalk Midblock roadway Combined

0 to 10 9 (7.1) 788 (17.2) 797 (16.9)

11 to 20 27 (21.3) 964 (21.0) 991 (21.1)

21 to 30 15 (11.8) 709 (15.5) 724 (15.4)

31 to 40 11 (8.7) 510 (11.1) 521 (11.1)

41 to 50 17 (13.4) 656 (14.3) 673 (14.3)

51 to 60 18 (14.2) 433 (9.5) 451 (9.6)

61 to 70 11 (8.7) 187 (4.1) 198 (4.2)

70+ 19 (15.0) 333 (7.3) 352 (7.5)

All 127 (100.0) 4,580 (100.0) 4,707 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

Table 24 presents the distribution of midblock pedestrian crashes in the GES data by pedestrian 

gender. As with the FARS data, there is a more even balance in pedestrian gender at midblock 

crossings (actually more females than males), but a substantially higher proportion of male 

pedestrians involved at noncrosswalk locations.
37

Table 24. GES midblock pedestrian crashes by pedestrian gender (2005–2009).

Pedestrian gender

Number (percent) of crashes by pedestrian gender

Midblock crosswalk Midblock roadway Combined

Male 58 (46.4) 2,908 (63.6) 2,966 (63.1)

Female 67 (53.6) 1,664 (36.4) 1,731 (36.9)

Unknown 2 — 8 — 10 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 25 summarizes the GES pedestrian crash data by the number of lanes crossed. While 

interesting, these data are not very meaningful without exposure data on the number of lanes 

crossed by pedestrians who were not killed in crashes. The same is true for table 26, which 

categorizes pedestrian crashes by the presence of a median, and thus a pedestrian refuge area, on 

the roadway crossed and table 27, which categorizes crashes by the posted speed limit at the 

crash location.

Table 25. GES midblock pedestrian crashes by number of travel lanes crossed (2005–2009).

Number of travel lanes crossed

Number (percent) of crashes by number of travel lanes crossed

Midblock crosswalk Midblock roadway Combined

One 9 (11.1) 208 (6.0) 217 (6.1)

Two 43 (53.1) 1,926 (55.9) 1,969 (55.8)

Three 6 (7.4) 436 (12.6) 442 (12.5)

Four 17 (21.0) 501 (14.5) 518 (14.7)

Five 5 (6.2) 302 (8.7) 307 (8.7)

Six 0 (0.0) 44 (1.3) 44 (1.2)

Seven or more 1 (1.2) 36 (1.0) 37 (1.0)

Unknown 46 — 1,127 — 1,173 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 26. GES midblock pedestrian crashes by presence of median (2005–2009).

Presence of median

Number (percent) of crashes by presence of median

Midblock crosswalk Midblock roadway Combined

Not physically divided (center 

two-way left turn lane) 3 (2.8) 236 (6.4) 239 (6.3)

Not physically divided (two 

way traffic) 80 (75.5) 2,390 (64.5) 2,470 (64.8)

Divided trafficway (median 

strip, barrier, etc) 22 (20.8) 864 (23.3) 886 (23.2)

One way traffic 1 (0.9) 216 (5.8) 217 (5.7)

Unknown 21 — 874 — 895 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
38

Table 27. GES midblock pedestrian crashes by posted speed limit (2005–2009).

Posted speed limit 

(mi/h)

Number (percent) of crashes by posted speed limit

Midblock crosswalk Midblock roadway Combined

No limit 33 (31.1) 125 (3.4) 158 (4.2)

5 0 (0.0) 11 (0.3) 11 (0.3)

10 1 (0.9) 18 (0.5) 19 (0.5)

15 5 (4.7) 75 (2.0) 80 (2.1)

20 5 (4.7) 92 (2.5) 97 (2.6)

25 22 (20.8) 918 (25.0) 940 (24.8)

30 13 (12.3) 553 (15.0) 566 (15.0)

35 21 (19.8) 863 (23.5) 884 (23.4)

40 2 (1.9) 318 (8.6) 320 (8.5)

45 3 (2.8) 371 (10.1) 374 (9.9)

50 0 (0.0) 60 (1.6) 60 (1.6)

55 1 (0.9) 122 (3.3) 123 (3.3)

60 0 (0.0) 75 (2.0) 75 (2.0)

65 0 (0.0) 50 (1.4) 50 (1.3)

70 0 (0.0) 24 (0.7) 24 (0.6)

75 0 (0.0) 3 (0.1) 3 (0.1)

Unknown 21 — 902 — 923 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 28 through table 31 present GES data for pedestrian crashes at midblock locations 

classified by weather condition, road surface condition, hour of the day, and light condition.

Table 28. GES midblock pedestrian crashes by weather condition (2005–2009).

Weather condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Clear/cloudy 111 (87.4) 4,077 (90.5) 4,188 (90.4)

Rain/sleet 14 (11.0) 354 (7.9) 368 (7.9)

Snow 0 (0.0) 47 (1.0) 47 (1.0)

Fog 0 (0.0) 8 (0.2) 8 (0.2)

Rain and fog 0 (0.0) 1 (0.0) 1 (0.0)

Other 2 (1.6) 18 (0.4) 20 (0.4)

Unknown 0 — 75 — 75 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 29. GES midblock pedestrian crashes by road surface condition (2005–2009).

Road surface condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Dry 104 (81.9) 3,880 (86.1) 3,984 (86.0)

Wet 20 (15.7) 551 (12.2) 571 (12.3)

Snowy, icy 3 (2.4) 72 (1.6) 75 (1.6)

Other 0 (0.0) 3 (0.1) 3 (0.1)

Unknown 0 — 74 — 74 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
39

Table 30. GES midblock pedestrian crashes by hour of day (2005–2009).

Hour of day

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

12–12:59 a.m. 0 (0.0) 133 (2.9) 133 (2.9)

1–1:59 a.m. 0 (0.0) 91 (2.0) 91 (2.0)

2–2:59 a.m. 0 (0.0) 108 (2.4) 108 (2.3)

3–3:59 a.m. 0 (0.0) 78 (1.7) 78 (1.7)

4—4:59 a.m. 0 (0.0) 41 (0.9) 41 (0.9)

5–5:59 a.m. 1 (0.8) 55 (1.2) 56 (1.2)

6–6:59 a.m. 2 (1.6) 94 (2.1) 96 (2.1)

7–7:59 a.m. 8 (6.3) 173 (3.8) 181 (3.9)

8–8:59 a.m. 10 (7.9) 122 (2.7) 132 (2.8)

9–9:59 a.m. 2 (1.6) 117 (2.6) 119 (2.6)

10–10:59 a.m. 12 (9.5) 111 (2.4) 123 (2.6)

11–11:59 a.m. 7 (5.6) 138 (3.0) 145 (3.1)

Noon–12:59 p.m. 5 (4.0) 169 (3.7) 174 (3.7)

1–1:59 p.m. 4 (3.2) 179 (3.9) 183 (3.9)

2–2:59 p.m. 13 (10.3) 236 (5.2) 249 (5.3)

3–3:59 p.m. 9 (7.1) 295 (6.5) 304 (6.5)

4–4:59 p.m. 11 (8.7) 335 (7.4) 346 (7.4)

5–5:59 p.m. 6 (4.8) 384 (8.5) 390 (8.4)

6–6:59 p.m. 6 (4.8) 402 (8.9) 408 (8.8)

7–7:59 p.m. 14 (11.1) 345 (7.6) 359 (7.7)

8–8:59 p.m. 5 (4.0) 281 (6.2) 286 (6.1)

9–9:59 p.m. 4 (3.2) 291 (6.4) 295 (6.3)

10–10:59 p.m. 5 (4.0) 218 (4.8) 223 (4.8)

11–11:59 pm 2 (1.6) 137 (3.0) 139 (3.0)

Unknown 1 — 47 — 48 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 31. GES midblock pedestrian crashes by light condition (2005–2009).

Light condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Daylight 90 (71.4) 2,429 (53.5) 2,519 (54.0)

Dusk-dawn 2 (1.6) 186 (4.1) 188 (4.0)

Dark-street lights 27 (21.4) 1,252 (27.6) 1,279 (27.4)

Dark-no street lights 7 (5.6) 670 (14.8) 677 (14.5)

Unknown 1 — 43 — 44 —

Total 127 (100.0) 4,580 (100.0) 4,707 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

California

HSIS data for California include crash data only for the State highway system, consisting of 

approximately 15,520 mi of highways. While some city streets and many small town “main 

streets” and suburban arterials are State highways, many of the areas with the highest 

concentrations of pedestrians in California are away from the State highway system. Data 

analyzed for this report include the years from 2006 to 2008, inclusive. Earlier years of data were 

not used because of changes in the coding of a key variable.
40

Table 32 shows that, during the study period, 526,898 crashes occurred on the California State 

highway system. Of these, 3,944 (0.7 percent) were pedestrian crashes. Nearly 70 percent of the 

pedestrian crashes occurred at midblock locations. Only 76 crashes (2.6 percent of the midblock 

crashes) were classified as occurring at midblock crosswalks.

Table 32. Summary of pedestrian crash data for California State highways (2006–2008).

Crashes

Number of crashes by year

2006 2007 2008 Combined

Total crashes 189,089 180,122 157,687 526,898

Pedestrian crashes 1,426 1,263 1,255 3,944

Pedestrian midblock crashes 1,057 932 917 2,906

Pedestrian midblock roadway crashes 768 637 650 2,055

Pedestrian midblock crossing crashes 20 24 32 76

Table 33 shows the relative frequencies of California crashes by midblock crosswalks and other 

midblock roadway locations. 

Table 33. Midblock pedestrian crashes on California State highways by pedestrian location 

(2006–2008).

Years

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

2006 20 (2.5) 768 (97.5) 788

2007 24 (3.6) 637 (96.4) 661

2008 32 (4.7) 650 (95.3) 682

Total 76 (3.6) 2,055 (96.4) 2,131

Percentages in this table are row percentages.

Table 34 classifies midblock pedestrian crashes in California by crash severity level. As 

observed in the GES data, crash severities appear to be lower for midblock crosswalk crashes 

than for other pedestrian crashes on the midblock roadway.

Table 34. Midblock pedestrian crashes on California State highways by injury severity 

(2006–2008).

Injury severity

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Fatal 1 (1.3) 517 (25.2) 518 (24.3)

Severe injury 15 (19.7) 447 (21.8) 462 (21.7)

Other visible injury 23 (30.3) 566 (27.5) 589 (27.6)

Complaint/pain 33 (43.4) 406 (19.8) 439 (20.6)

Property damage only 4 (5.3) 119 (5.8) 123 (5.8)

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

Table 35 classifies midblock pedestrian crashes by whether they occurred on a divided or 

undivided roadway. Approximately 75 percent of pedestrian midblock crashes occurred on an 

undivided roadway.
41

Table 35. Midblock pedestrian crashes on California State highways by presence of median

(2006–2008).

Presence of median

Number (percent) of crashes by presence of median

Midblock crosswalk Midblock roadway Combined

Undivided 56 (73.7) 1,551 (76.2) 1,607 (76.1)

Divided 20 (26.3) 484 (23.8) 504 (23.9)

Unknown 0 — 20 — 20 —

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 36 classifies midblock pedestrian crashes in California by whether traffic control devices 

were present and functioning. Most pedestrian crashes, including midblock crosswalk crashes, 

occurred at locations with no controls present. Presumably, this refers to traffic signals or stop 

signs, because clearly, crosswalk markings (by definition) and signing were present at marked 

crosswalks.

Table 36. Midblock pedestrian crashes on California State highways by traffic control 

device operating (2006–2008).

Traffic control device 

operating

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Control functioning 32 (42.1) 305 (14.8) 337 (15.8)

Control not functioning 1 (1.3) 7 (0.3) 8 (0.4)

Controls obscured 0 (0.0) 1 (0.0) 1 (0.0)

No controls present 43 (56.6) 1,742 (84.9) 1,785 (83.8)

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

Table 37 through table 43 present California data for pedestrian crashes at midblock locations 

classified by collision factors (contributing circumstances), weather condition, vehicle type at 

fault, road surface condition, light condition, hour of the day, and day of the week.
42

Table 37. Midblock pedestrian crashes on California State highways by collision factor 

(2006–2008).

Number (percent) of crashes by pedestrian location

Collision factor Midblock crosswalk Midblock roadway Combined

Driving/bicycling under 

influence 1 (1.4) 119 (6.1) 120 (6.0)

Impeding traffic 0 (0.0) 7 (0.4) 7 (0.3)

Unsafe speed 2 (2.8) 379 (19.5) 381 (18.9)

Following too closely 0 (0.0) 3 (0.2) 3 (0.1)

Wrong side of road 0 (0.0) 11 (0.6) 11 (0.5)

Improper passing 0 (0.0) 10 (0.5) 10 (0.5)

Unsafe lane change 0 (0.0) 17 (0.9) 17 (0.8)

Improper turning 3 (4.2) 210 (10.8) 213 (10.6)

Auto right of way 1 (1.4) 7 (0.4) 8 (0.4)

Pedestrian right of way 52 (73.2) 35 (1.8) 87 (4.3)

Pedestrian violation 11 (15.5) 1,011 (52.1) 1,022 (50.8)

Traffic signals and signs 0 (0.0) 2 (0.1) 2 (0.1)

Hazardous parking 0 (0.0) 8 (0.4) 8 (0.4)

Brakes 0 (0.0) 1 (0.1) 1 (0.0)

Other equipment 0 (0.0) 5 (0.3) 5 (0.2)

Other hazard violations 0 (0.0) 13 (0.7) 13 (0.6)

Other than driver (or 

pedestrian) 0 (0.0) 48 (2.5) 48 (2.4)

Unsafe start/brake 1 1.4 48 (2.5) 49 (2.4)

Other improper driving 0 (0.0) 5 (0.3) 5 (0.2)

Pedestrian/other under 

influence 0 (0.0) 1 (0.1) 1 (0.0)

Unknown 5 — 115 — 120 —

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 38. Midblock pedestrian crashes on California State highways by weather condition 

(2006–2008).

Weather condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Clear 62 (81.6) 1,608 (78.6) 1,670 (78.7)

Cloudy 12 (15.8) 325 (15.9) 337 (15.9)

Raining 2 (2.6) 70 (3.4) 72 (3.4)

Snowing 0 (0.0) 19 (0.9) 19 (0.9)

Fog 0 (0.0) 21 (1.0) 21 (1.0)

Other 0 (0.0) 2 (0.1) 2 (0.1)

Unknown 0 — 10 — 10 —

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
43

Table 39. Midblock pedestrian crashes on California State highways by vehicle type at 

fault (2006–2008).

Vehicle type at fault

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Not applicable 11 (14.5) 228 (11.1) 239 (11.2)

Passenger car/station wagon 46 (60.5) 536 (26.1) 582 (27.3)

Passenger car w/trailer 0 (0.0) 1 (0.0) 1 (0.0)

Motorcycle 0 (0.0) 12 (0.6) 12 (0.6)

Pickup/panel truck 9 (11.8) 146 (7.1) 155 (7.3)

Pickup/panel truck w/trailer 0 (0.0) 7 (0.3) 7 (0.3)

Truck/truck tractor 0 (0.0) 21 (1.0) 21 (1.0)

Truck tractor w/1 trailer 0 (0.0) 42 (2.0) 42 (2.0)

Other bus 0 (0.0) 4 (0.2) 4 (0.2)

Emergency vehicle 0 (0.0) 3 (0.1) 3 (0.1)

Highway construction equipment 0 (0.0) 1 (0.0) 1 (0.0)

Bicycle 0 (0.0) 3 (0.1) 3 (0.1)

Other motor vehicle 0 (0.0) 4 (0.2) 4 (0.2)

Other non-motor vehicle 10 (13.2) 1,047 (50.9) 1,057 (49.6)

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

Table 40. Midblock pedestrian crashes on California State highways by road surface 

condition (2006–2008).

Road surface condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Dry 68 (89.5) 1,805 (88.2) 1,873 (88.2)

Wet 8 (10.5) 175 (8.5) 183 (8.6)

Snowy, icy 0 (0.0) 63 (3.1) 63 (3.0)

Slippery/muddy 0 (0.0) 4 (0.2) 4 (0.2)

Unknown 0 — 8 — 8 —

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 41. Midblock pedestrian crashes on California State highways by light condition 

(2006–2008).

Light condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Daylight 40 (52.6) 675 (33.0) 715 (33.7)

Dusk-dawn 3 (3.9) 62 (3.0) 65 (3.1)

Dark-street lights 23 (30.3) 622 (30.4) 645 (30.4)

Dark-no street lights 7 (9.2) 672 (32.8) 679 (32.0)

Dark-light not functioning 3 (3.9) 15 (0.7) 18 (0.8)

Unknown 0 — 9 — 9 —

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
44

Table 42. Midblock pedestrian crashes on California State highways by hour of day

(2006-2008).

Hour of day

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

12–12:59 a.m. 1 (1.3) 88 (4.3) 89 (4.2)

1–1:59 a.m. 0 (0.0) 94 (4.6) 94 (4.4)

2–2:59 a.m. 1 (1.3) 112 (5.5) 113 (5.3)

3–3:59 a.m. 0 (0.0) 87 (4.2) 87 (4.1)

4–4:59 a.m. 0 (0.0) 59 (2.9) 59 (2.8)

5–5:59 a.m. 1 (1.3) 64 (3.1) 65 (3.1)

6–6:59 a.m. 2 (2.6) 45 (2.2) 47 (2.2)

7–7:59 a.m. 4 (5.3) 50 (2.4) 54 (2.5)

8–8:59 a.m. 5 (6.6) 34 (1.7) 39 (1.8)

9–9:59 a.m. 0 (0.0) 56 (2.7) 56 (2.6)

10–10:59 a.m. 1 (1.3) 43 (2.1) 44 (2.1)

11–11:59 a.m. 4 (5.3) 38 (1.9) 42 (2.0)

Noon–12:59 p.m. 0 (0.0) 60 (2.9) 60 (2.8)

1–1:59 p.m. 2 (2.6) 66 (3.2) 68 (3.2)

2–2:59 p.m. 7 (9.2) 69 (3.4) 76 (3.6)

3–3:59 p.m. 7 (9.2) 85 (4.1) 92 (4.3)

4–4:59 p.m. 3 (3.9) 90 (4.4) 93 (4.4)

5–5:59 p.m. 8 (10.5) 91 (4.4) 99 (4.7)

6–6:59 p.m. 11 (14.5) 133 (6.5) 144 (6.8)

7–7:59 pm 5 (6.6) 132 (6.4) 137 (6.4)

8–8:59 p.m. 5 (6.6) 146 (7.1) 151 (7.1)

9–9:59 p.m. 2 (2.6) 147 (7.2) 149 (7.0)

10–10:59 p.m. 5 (6.6) 146 (7.1) 151 (7.1)

11–11:59 p.m. 2 (2.6) 118 (5.7) 120 (5.6)

Unknown 0 — 2 — 2 —

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 43. Midblock pedestrian crashes on California State highways by day of the week 

(2006–2008).

Day of week

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Sunday 9 (11.8) 295 (14.4) 304 (14.3)

Monday 9 (11.8) 267 (13.0) 276 (13.0)

Tuesday 18 (23.7) 266 (12.9) 284 (13.3)

Wednesday 10 (13.2) 237 (11.5) 247 (11.6)

Thursday 12 (15.8) 332 (16.2) 344 (16.1)

Friday 10 (13.2) 349 (17.0) 359 (16.8)

Saturday 8 (10.5) 309 (15.0) 317 (14.9)

Total 76 (100.0) 2,055 (100.0) 2,131 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
45

Minnesota

HSIS data for Minnesota include crash data for nearly all crashes statewide, including those that 

occurred on both State-maintained and local-agency-maintained road systems. Data analyzed for 

this report include the years 2003 to 2007, inclusive.

Table 44 shows that, during the study period, 406,786 crashes occurred in Minnesota. Of these, 

8,271 (2.0 percent) were pedestrian crashes. Approximately 29 percent of these pedestrian 

crashes occurred at midblock locations. Only 71 pedestrian crashes (3.0 percent of all midblock 

pedestrian crashes) are classified as occurring at midblock crosswalks.

Table 45 shows the relative frequency of Minnesota crashes by midblock crosswalk and other 

midblock roadway locations.

Table 46 classifies midblock pedestrian crashes in Minnesota by crash severity level. In contrast 

to other States, the proportion of fatal and incapacitating injury crashes is higher for midblock 

crosswalks than for other midblock locations in Minnesota, but the sample size for midblock 

crosswalk crashes is so low that this may not be a valid comparison.

Table 44. Summary of pedestrian crash data for Minnesota State highways (2003–2007).

Crashes

Number of crashes by year

2003 2004 2005 2006 2007 Combined

Total crashes 79,825 86,719 85,447 77,243 77,552 406,786

Pedestrian crashes 1,125 1,500 1,844 1,909 1,893 8,271

Pedestrian midblock crashes 377 459 520 492 541 2,389

Pedestrian midblock roadway 

crashes 277 318 337 313 353 1,598

Pedestrian midblock crossing crashes 14 13 20 11 13 71

Table 45. Midblock pedestrian crashes on Minnesota State highways by pedestrian location 

(2003–2007).

Year

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

2003 14 (4.8) 277 (95.2) 291

2004 13 (3.9) 318 (96.1) 331

2005 20 (5.6) 337 (94.4) 357

2006 11 (3.4) 313 (96.6) 324

2007 13 (3.5) 353 (96.5) 366

Total 71 (4.3) 1,598 (95.7) 1,669

Percentages in this table are row percentages.
46

Table 46. Midblock pedestrian crashes on Minnesota State highways by injury severity 

(2003–2007).

Injury severity

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Fatal 0 (0.0) 14 (0.9) 14 (0.8)

Incapacitating injury 5 (7.0) 55 (3.5) 60 (3.6)

Non-incapacitating injury 8 (11.3) 192 (12.1) 200 (12.0)

Possible injury 15 (21.1) 367 (23.0) 382 (23.0)

Property damage only 43 (60.6) 965 (60.6) 1008 (60.6)

Injury unknown 0 — 5 — 5 —

Total 71 (100.0) 1,598 (100.0) 1,669 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 47 classifies midblock pedestrian crashes in Minnesota by the type of traffic control 

device. The data for midblock roadways in table 45 include a substantial number of pedestrian 

crashes at either signals or stop signs; these crashes must either have occurred at nonintersection 

signals or stop signs (i.e., driveways) or the basic intersection versus nonintersection 

classification of the crashes is incorrect. Table 48 through table 52 present Minnesota data for 

pedestrian crashes at midblock locations classified by weather condition, road surface condition, 

light condition, hour of the day, and day of the week.

Table 47. Midblock pedestrian crashes on Minnesota State highways by traffic control 

device operating (2003–2007).

Traffic control device operating

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Traffic signals 8 11.3 253 (16.0) 261 (15.8)

Overhead flashers 2 2.8 1 (0.1) 3 (0.2)

Stop sign—all approaches 0 (0.0) 25 (1.6) 25 (1.5)

Stop sign—other 14 19.7 201 (12.7) 215 (13.0)

Yield sign 1 1.4 21 (1.3) 22 (1.3)

Officer, flagman, or school patrol 0 (0.0) 2 (0.1) 2 (0.1)

School bus stop arm 0 (0.0) 3 (0.2) 3 (0.2)

School zone sign 0 (0.0) 3 (0.2) 3 (0.2)

No passing zone 1 1.4 8 (0.5) 9 (0.5)

Railroad crossing—flashing lights 0 (0.0) 2 (0.1) 2 (0.1)

Railroad crossing—overhead 

flashers and gates 0 (0.0) 2 (0.1) 2 (0.1)

Other 4 5.6 9 (0.6) 13 (0.8)

Not applicable 41 57.7 1056 (66.6) 1097 (66.2)

Unknown 0 — 12 — 12 —

Total 71 (100.0) 1,598 (100.0) 1,669 (100.0)

— Indicates count not included in calculation of percentage

Column percentages may not appear to add to 100 percent due to rounding.
47

Table 48. Midblock pedestrian crashes on Minnesota State highways by weather condition

(2003–2007).

Weather condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Clear 38 (53.5) 796 (50.2) 834 (50.4)

Cloudy 19 (26.8) 483 (30.5) 502 (30.3)

Raining 2 (2.8) 31 (2.0) 33 (2.0)

Snowing 10 (14.1) 168 (10.6) 178 (10.7)

Sleet/hail/freezing rain 0 (0.0) 67 (4.2) 67 (4.0)

Fog/smog/dust 0 (0.0) 7 (0.4) 7 (0.4)

Blowing sand/dust/snow 2 (2.8) 24 (1.5) 26 (1.6)

Severe crosswinds 0 (0.0) 3 (0.2) 3 (0.2)

Other 0 (0.0) 6 (0.4) 6 (0.4)

Unknown 0 — 13 — 13 —

Total 71 (100.0) 1,598 (100.0) 1,669 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 49. Midblock pedestrian crashes on Minnesota State highways by road surface 

condition (2003–2007).

Road surface condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Dry 26 (36.6) 740 (46.6) 766 (46.2)

Wet 6 (8.5) 197 (12.4) 203 (12.2)

Snow 12 (16.9) 185 (11.7) 197 (11.9)

Slush 2 (2.8) 54 (3.4) 56 (3.4)

Ice/packed snow 23 (32.4) 391 (24.6) 414 (25.0)

Debris 1 (1.4) 0 (0.0) 1 (0.1)

Other 1 (1.4) 20 (1.3) 21 (1.3)

Unknown 0 — 11 — 11 —

Total 71 (100.0) 1,598 (100.0) 1,669 (100.0)

— Indicates count not included in calculation of percentage

Column percentages may not appear to add to 100 percent due to rounding.

Table 50. Midblock pedestrian crashes on Minnesota State highways by light condition 

(2003–2007).

Light condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Daylight 37 (52.1) 944 (59.4) 981 (59.1)

Dusk-dawn 4 (5.6) 93 (5.8) 97 (5.8)

Dark-street lights on 22 (31.0) 386 (24.3) 408 (24.6)

Dark-no street lights 7 (9.9) 153 (9.6) 160 (9.6)

Dark-unknown lighting 1 (1.4) 13 (0.8) 14 (0.8)

Other 0 (0.0) 2 (0.1) 2 (0.1)

Unknown 0 — 7 — 7 —

Total 71 (100.0) 1,598 (100.0) 1,669 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
48

Table 51. Midblock pedestrian crashes on Minnesota State highways by hour of day

(2003–2007).

Hour of day

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

12–12:59 a.m. 2 (2.8) 27 (1.7) 29 (1.7)

1–1:59 a.m. 1 (1.4) 17 (1.1) 18 (1.1)

2–2:59 a.m. 1 (1.4) 32 (2.0) 33 (2.0)

3–3:59 a.m. 0 (0.0) 12 (0.8) 12 (0.7)

4–4:59 a.m. 0 (0.0) 10 (0.6) 10 (0.6)

5–5:59 a.m. 1 (1.4) 15 (0.9) 16 (1.0)

6–6:59 a.m. 1 (1.4) 44 (2.8) 45 (2.7)

7–7:59 a.m. 3 (4.2) 98 (6.1) 101 (6.1)

8–8:59 a.m. 3 (4.2) 92 (5.8) 95 (5.7)

9–9:59 a.m. 2 (2.8) 75 (4.7) 77 (4.6)

10–10:59 a.m. 2 (2.8) 70 (4.4) 72 (4.3)

11–11:59 a.m. 3 (4.2) 75 (4.7) 78 (4.7)

Noon–12:59 p.m. 4 (5.6) 81 (5.1) 85 (5.1)

1–1:59 p.m. 5 (7.0) 74 (4.6) 79 (4.7)

2–2:59 p.m. 2 (2.8) 113 (7.1) 115 (6.9)

3–3:59 p.m. 5 (7.0) 148 (9.3) 153 (9.2)

4–4:59 p.m. 7 (9.9) 118 (7.4) 125 (7.5)

5–5:59 p.m. 12 (16.9) 147 (9.2) 159 (9.5)

6–6:59 p.m. 0 (0.0) 95 (6.0) 95 (5.7)

7–7:59 p.m. 4 (5.6) 68 (4.3) 72 (4.3)

8–8:59 p.m. 2 (2.8) 61 (3.8) 63 (3.8)

9–9:59 p.m. 5 (7.0) 49 (3.1) 54 (3.2)

10–10:59 p.m. 6 (8.5) 52 (3.3) 58 (3.5)

11–11:59 p.m. 0 (0.0) 21 (1.3) 21 (1.3)

Unknown 0 — 4 — 4 —

Total 71 (100.0) 1,598 (100.0) 1,669 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 52. Midblock pedestrian crashes on Minnesota State highways by day of the week 

(2003–2007).

Day of week

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Sunday 7 (9.9) 190 (11.9) 197 (11.8)

Monday 6 (8.5) 228 (14.3) 234 (14.0)

Tuesday 9 (12.7) 239 (15.0) 248 (14.9)

Wednesday 12 (16.9) 257 (16.1) 269 (16.1)

Thursday 9 (12.7) 230 (14.4) 239 (14.3)

Friday 16 (22.5) 244 (15.3) 260 (15.6)

Saturday 12 (16.9) 210 (13.1) 222 (13.3)

Total 71 (100.0) 1,598 (100.0) 1,669 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

North Carolina

HSIS data for North Carolina include crash data for approximately 62,000 mi of the 77,000 mi of 

roadway on the State-maintained highway system. The State-maintained road system in North 

Carolina is much larger than the State-maintained road systems of most other States and includes 
49

many roads that would be county roads in other States. The available data cover approximately 

two-thirds of the total public road mileage in the State. Data analyzed for this report include the 

years from 2005 to 2008, inclusive.

Table 53 shows that, during the study period, 579,654 crashes occurred in North Carolina. Of 

these, 3,847 (0.7 percent) were pedestrian crashes. Nearly 85 percent of these pedestrian crashes 

occurred at midblock locations. Only 86 crashes (2.7 percent of the midblock crashes) were 

classified as occurring at midblock crosswalks.

Table 53. Summary of pedestrian crash data for North Carolina State highways

(2005–2008).

Crashes

Number of crashes by year

2005 2006 2007 2008 Combined

Total crashes 136,842 146,333 149,964 146,515 579,654

Pedestrian crashes 888 994 975 990 3,847

Pedestrian midblock crashes 750 837 824 828 3,239

Pedestrian midblock roadway crashes 630 682 662 665 2,639

Pedestrian midblock crossing crashes 15 27 22 22 86

Table 54 shows the relative frequencies of North Carolina crashes by midblock crosswalk and 

other midblock roadway locations.

Table 54. Midblock pedestrian crashes on North Carolina State highways by pedestrian 

location (2005–2008).

Years

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

2005 15 (2.3) 630 (97.7) 645

2006 27 (3.8) 682 (96.2) 709

2007 22 (3.2) 662 (96.8) 684

2008 22 (3.2) 665 (96.8) 687

Total 86 (3.2) 2,639 (96.8) 2,725

Percentages in this table are row percentages.

Table 55 classifies midblock pedestrian crashes in North Carolina by crash severity level.

Table 56 classifies midblock pedestrian crashes in North Carolina by the pedestrian action prior 

to the crash. Table 57 presents similar data for contributing factors, many of which are related to 

pedestrian actions.

Table 55. Midblock pedestrian crashes on North Carolina State highways by injury 

severity (2005–2008).

Injury severity

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Fatal 2 (2.4) 12 (0.5) 14 (0.5)

Incapacitating injury 2 (2.4) 15 (0.6) 17 (0.6)

Non-incapacitating injury 4 (4.8) 176 (6.8) 180 (6.7)

Possible injury 17 (20.5) 589 (22.7) 606 (22.6)

Property damage only 58 (69.9) 1806 (69.5) 1864 (69.5)

Injury unknown 3 — 41 — 44 —

Total 86 (100.0) 2,639 (100.0) 2,725 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
50

Table 56. Midblock pedestrian crashes on North Carolina State highways by pedestrian 

action (2005–2008).

Collision factor

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Entering or crossing 

specified location 41 (48.2) 685 (26.0) 726 (26.7)

Walking, riding, running 

with traffic 8 (9.4) 591 (22.4) 599 (22.0)

Walking, riding, running 

against traffic 6 (7.1) 357 (13.6) 363 (13.4)

Working 13 (15.3) 84 (3.2) 97 (3.6)

Pushing vehicle 2 (2.4) 37 (1.4) 39 (1.4)

Approaching or leaving 

vehicle 1 (1.2) 105 (4.0) 106 (3.9)

Playing 2 (2.4) 25 (0.9) 27 (1.0)

Standing 3 (3.5) 290 (11.0) 293 (10.8)

Other 9 (10.6) 459 (17.4) 468 (17.2)

Unknown 1 — 6 — 7 —

Total 86 (100.0) 2,639 (100.0) 2,725 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 57. Midblock pedestrian crashes on North Carolina State highways by contributing 

factor (2005–2008).

Contributing factor

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Coming from behind parked 

vehicle 1 (1.3) 41 (1.6) 42 (1.6)

Darting 10 (12.5) 461 (18.1) 471 (18.0)

Lying and/or illegally in 

roadway 1 (1.3) 382 (15.0) 383 (14.6)

Failure to yield right of way 6 (7.5) 348 (13.7) 354 (13.5)

Not visible (dark clothing) 6 (7.5) 306 (12.0) 312 (11.9)

Inattentive 2 (2.5) 74 (2.9) 76 (2.9)

Failure to obey traffic signs, 

signals 2 (2.5) 23 (0.9) 25 (1.0)

Wrong side of road 3 (3.8) 125 (4.9) 128 (4.9)

Other 5 (6.3) 156 (6.1) 161 (6.1)

None 44 (55.0) 626 (24.6) 670 (25.6)

Unknown 6 — 97 — 103 —

Total 86 (100.0) 2,639 (100.0) 2,725 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 58 through table 62 present North Carolina data for pedestrian crashes at midblock 

locations classified by weather condition, road surface condition, light condition, hour of the day, 

and day of week.
51

Table 58. Midblock pedestrian crashes on North Carolina State highways by weather 

condition (2005–2008).

Weather condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Clear 60 (69.8) 1824 (69.1) 1884 (69.1)

Cloudy 13 (15.1) 501 (19.0) 514 (18.9)

Raining 11 (12.8) 220 (8.3) 231 (8.5)

Snowing 1 (1.2) 30 (1.1) 31 (1.1)

Fog, smog, smoke 0 (0.0) 31 (1.2) 31 (1.1)

Sleet or hail 0 (0.0) 32 (1.2) 32 (1.2)

Severe crosswinds 1 (1.2) 0 (0.0) 1 (0.0)

Blowing sand, dirt, snow 0 (0.0) 1 (0.0) 1 (0.0)

Unknown 0 — 0 — 0 —

Total 86 (100.0) 2,639 (100.0) 2,725 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 59. Midblock pedestrian crashes on North Carolina State highways by road surface 

condition (2005–2008).

Road surface condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Dry 66 (76.7) 2,100 (79.8) 2,166 (79.7)

Wet 20 (23.3) 459 (17.4) 479 (17.6)

Water (standing, moving) 0 (0.0) 10 (0.4) 10 (0.4)

Ice 0 (0.0) 26 (1.0) 26 (1.0)

Snow 0 (0.0) 23 (0.9) 23 (0.8)

Slush 0 (0.0) 11 (0.4) 11 (0.4)

Sand, mud, dirt, gravel 0 (0.0) 1 (0.0) 1 (0.0)

Fuel, oil 0 (0.0) 1 (0.0) 1 (0.0)

Unknown 0 — 8 — 8 —

Total 86 (100.0) 2,639 (100.0) 2,725 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 60. Midblock pedestrian crashes on North Carolina State highways by light 

condition (2005–2008).

Light condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Daylight 58 (67.4) 1,884 (71.4) 1,942 (71.3)

Dusk-dawn 7 (8.2) 130 (4.9) 137 (5.0)

Dark-street lights on 17 (19.8) 387 (14.7) 404 (14.8)

Dark-no street lights 4 (4.7) 231 (8.8) 235 (8.6)

Dark-unknown lighting 0 (0.0) 5 (0.2) 5 (0.2)

Other 0 (0.0) 1 (0.0) 1 (0.0)

Unknown 0 — 1 — 1 —

Total 86 (100.0) 2,639 (100.0) 2,725 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
52

Table 61. Midblock pedestrian crashes on North Carolina State highways by day of the 

week (2005–2008).

Day of week

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Sunday 4 (4.7) 151 (5.7) 155 (5.7)

Monday 20 (23.3) 382 (14.5) 402 (14.8)

Tuesday 11 (12.8) 412 (15.6) 423 (15.5)

Wednesday 10 (11.6) 434 (16.4) 444 (16.3)

Thursday 20 (23.3) 490 (18.6) 510 (18.7)

Friday 16 (18.6) 514 (19.5) 530 (19.4)

Saturday 5 (5.8) 256 (9.7) 261 (9.6)

Total 86 (100.0) 2,639 (100.0) 2,725 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

Table 62. Midblock pedestrian crashes on North Carolina State highways by hour of day 

(2005–2008).

Hour of day

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

12–12:59 a.m. 2 (2.3) 21 (0.8) 23 (0.8)

1–1:59 a.m. 1 (1.2) 14 (0.5) 15 (0.6)

2–2:59 a.m. 0 (0.0) 27 (1.0) 27 (1.0)

3–3:59 a.m. 0 (0.0) 16 (0.6) 16 (0.6)

4–4:59 a.m. 0 (0.0) 17 (0.6) 17 (0.6)

5–5:59 a.m. 0 (0.0) 22 (0.8) 22 (0.8)

6–6:59 a.m. 2 (2.3) 87 (3.3) 89 (3.3)

7–7:59 a.m. 6 (7.0) 176 (6.7) 182 (6.7)

8–8:59 a.m. 12 (14.0) 164 (6.2) 176 (6.5)

9–9:59 a.m. 3 (3.5) 131 (5.0) 134 (4.9)

10–10:59 a.m. 5 (5.8) 123 (4.7) 128 (4.7)

11–11:59 a.m. 3 (3.5) 161 (6.1) 164 (6.0)

Noon–12:59 p.m. 6 (7.0) 186 (7.0) 192 (7.0)

1–1:59 p.m. 4 (4.7) 203 (7.7) 207 (7.6)

2–2:59 p.m. 8 (9.3) 190 (7.2) 198 (7.3)

3–3:59 p.m. 5 (5.8) 228 (8.6) 233 (8.6)

4–4:59 p.m. 7 (8.1) 226 (8.6) 233 (8.6)

5–5:59 p.m. 4 (4.7) 208 (7.9) 212 (7.8)

6–6:59 p.m. 8 (9.3) 162 (6.1) 170 (6.2)

7–7:59 p.m. 3 (3.5) 99 (3.8) 102 (3.7)

8–8:59 p.m. 3 (3.5) 64 (2.4) 67 (2.5)

9–9:59 p.m. 2 (2.3) 51 (1.9) 53 (1.9)

10–10:59 p.m. 0 (0.0) 35 (1.3) 35 (1.3)

11–11:59 p.m. 2 (2.3) 28 (1.1) 30 (1.1)

Total 86 — 2,639 — 2,725 —

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Ohio

HSIS data for Ohio include crash data only for the State highway system, consisting of 

approximately 19,500 mi of highways. While some city streets and many small town “main 

streets” and suburban arterials are State highways, many areas with the highest concentrations of 

pedestrians in Ohio are not on the State highway system. Data analyzed for this report include 

the years from 2005 to 2008, inclusive.
53

Table 63 shows that during the study period, 699,595 crashes occurred on the Ohio State 

highway system. Of these, 4,127 (0.6 percent) were pedestrian crashes. Approximately 

45 percent of the pedestrian crashes occurred at midblock locations. Only 22 crashes (1.2 percent 

of the midblock crashes) were classified as occurring at midblock crosswalks.

Table 63. Summary of pedestrian crash data for Ohio State highways (2005–2009).

Crashes Number of crashes by year

2005 2006 2007 2008 2009 Combined

Total crashes 143,134 144,821 145,810 128,625 137,205 699,595

Pedestrian crashes 705 831 820 873 898 4,127

Pedestrian midblock crashes 334 368 358 378 400 1,838

Pedestrian midblock roadway crashes 102 147 129 123 129 630

Pedestrian midblock crossing crashes 4 1 7 3 7 22

Table 64 shows the relative frequencies of Ohio crashes by midblock crosswalk and other 

midblock roadway locations. Table 65 classifies midblock pedestrian crashes in Ohio by crash 

severity level.

Table 64. Midblock pedestrian crashes on Ohio State highways by pedestrian location

(2005–2009).

Year

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

2005 4 (3.8) 102 (96.2) 106

2006 1 (0.1) 147 (99.9) 148

2007 7 (5.1) 129 (94.9) 136

2008 3 (2.4) 123 (97.6) 126

2009 7 (5.2) 129 (94.8) 136

Total 22 (3.4) 630 (96.6) 652

Percentages in this table are row percentages.

Table 65. Midblock pedestrian crashes on Ohio State highways by injury severity

(2005–2009).

Injury severity

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Fatal 0 (0.0) 75 (11.9) 75 (11.5)

Incapacitating injury 1 (4.5) 168 (26.7) 169 (25.9)

Non-incapacitating injury 14 (63.7) 213 (33.8) 227 (34.9)

Possible injury 7 (31.8) 123 (19.5) 130 (19.9)

Property damage only 0 (0.0) 51 (8.1) 51 7.8)

Total 22 (100.0) 630 (100.0) 652 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

Table 66 classified midblock pedestrian crashes in Ohio by the traffic control devices present. 

The table for Ohio differs markedly from the tables for other States because pavement markings 

are included as one of the traffic control devices that may be present.
54

Table 66. Midblock pedestrian crashes on Ohio State highways by traffic control device 

(2005–2009).

Traffic control device

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

No controls 10 (45.5) 251 (46.7) 261 (46.6)

Traffic signal 4 (18.2) 15 (2.8) 19 (3.4)

Traffic flashers 0 (0.0) 1 (0.2) 1 (0.2)

Construction barricades 0 (0.0) 3 (0.6) 3 (0.5)

Police officer 0 (0.0) 2 (0.4) 2 (0.4)

Pavement markings 2 (9.1) 231 (42.9) 233 (41.6)

Crosswalk lines 5 (22.7) 17 (3.2) 22 (3.9)

Walk/don’t walk signal 1 (4.5) 12 (2.2) 13 (2.3)

Other 0 (0.0) 6 (1.1) 6 (1.1)

Unknown 0 — 92 — 92 —

Total 22 (100.0) 630 (100.0) 652 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 67 classifies midblock pedestrian crashes in Ohio by pedestrian action that contributed to 

the crash. Table 68 through table 74 present Ohio data for pedestrian crashes at midblock 

locations by number of lanes crossed, presence of a median, weather condition, road surface 

condition, light condition, hour of the day, and day of the week.

Table 67. Midblock pedestrian crashes on Ohio State highways by pedestrian action

(2005–2009).

Collision factor

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

None 10 (71.5) 63 (11.5) 73 (13.0)

Operating defective equipment 0 (0.0) 1 (0.2) 1 (0.2)

Improper crossing 2 (14.3) 238 (43.5) 240 (42.8)

Darting 1 (7.1) 76 (13.9) 77 (13.8)

Lying and/or illegally in roadway 0 (0.0) 54 (9.9) 54 (9.6)

Failure to yield 0 (0.0) 28 (4.9) 28 (4.8)

Not visible (dark clothing) 0 (0.0) 23 (4.2) 23 (4.1)

Inattentive 0 (0.0) 22 (4.0) 22 (3.9)

Failure to obey traffic signs, signals, etc. 0 (0.0) 5 (0.9) 5 (0.9)

Wrong side of the road 0 (0.0) 13 (2.4) 13 (2.3)

Other 1 (7.1) 25 (4.6) 26 (4.6)

Unknown 8 — 82 — 90 —

Total 22 (100.0) 630 (100.0) 652 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
55

Table 68. Midblock pedestrian crashes on Ohio State highways by number of lanes crossed 

(2005–2009).

Number of 

lanes

Number (percent) of crashes by number of lanes crossed

Midblock Crosswalk Midblock Roadway Combined

2 4 (18.2) 211 (33.5) 215 (33.0)

3 0 (0.0) 16 (2.5) 16 (2.5)

4 17 (77.3) 342 (54.3) 359 (55.1)

5 0 (0.0) 3 (0.5) 3 (0.5)

6 1 (4.5) 45 (7.1) 46 (7.1)

7 0 (0.0) 1 (0.2) 1 (0.2)

8 0 (0.0) 11 (1.7) 11 (1.7)

9 0 (0.0) 1 (0.2) 1 (0.2)

Unknown 0 — 0 — 0 —

Total 22 (100.0) 630 (100.0) 652 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 69. Midblock pedestrian crashes on Ohio State highways by presence of median 

(2005–2009).

Presence of median

Number (percent) of crashes by presence of median

Midblock Crosswalk Midblock Roadway Combined

Divided 1 (4.5) 101 (16.0) 102 (15.6)

Undivided 21 (95.5) 529 (84.0) 550 (84.4)

Total 22 (100.0) 630 (100.0) 652 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

Table 70. Midblock pedestrian crashes on Ohio State highways by weather condition 

(2005–2009).

Weather condition

Number (percent) of crashes by pedestrian location

Midblock Crosswalk Midblock Roadway Combined

Clear 19 (86.4) 531 (85.0) 550 (85.0)

Cloudy 2 (9.1) 69 (11.0) 71 (11.0)

Raining 0 (0.0) 12 (1.9) 12 (1.9)

Snowing 0 (0.0) 2 (0.3) 2 (0.3)

Sleet or hail 1 (4.5) 11 (1.8) 12 (1.9)

Unknown 0 — 5 — 5 —

Total 22 (100.0) 630 (100.0) 652 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
56

Table 71. Midblock pedestrian crashes on Ohio State highways by road surface condition 

(2005–2009).

Road surface condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Dry 17 (77.3) 488 (78.0) 505 (77.9)

Wet 4 (18.2) 118 (18.8) 122 (18.8)

Water 1 (4.5) 12 (1.9) 13 (2.0)

Snowy, icy 0 (0.0) 8 (1.3) 8 (1.3)

Unknown 0 — 7 — 7 —

Total 22 (100.0) 630 (100.0) 652 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 72. Midblock pedestrian crashes on Ohio State highways by light condition

(2005–2009).

Light condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Daylight 16 (72.7) 297 (47.7) 313 (48.5)

Dusk-dawn 2 (9.1) 26 (4.1) 28 (4.4)

Dark-street lights on 4 (18.2) 193 (31.0) 197 (30.5)

Dark-no street lights 0 (0.0) 107 (17.2) 107 (16.6)

Unknown 0 — 7 — 7 —

Total 22 (100.0) 630 (100.0) 652 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 73. Midblock pedestrian crashes on Ohio State highways by day of the week

(2005–2009).

Day of week

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Sunday 1 (4.5) 76 (12.1) 77 (11.8)

Monday 5 (22.7) 75 (11.9) 80 (12.3)

Tuesday 3 (13.6) 102 (16.2) 105 (16.1)

Wednesday 3 (13.6) 91 (14.4) 94 (14.4)

Thursday 5 (22.7) 95 (15.1) 100 (15.3)

Friday 3 (13.6) 103 (16.3) 106 (16.3)

Saturday 2 (9.1) 88 (14.0) 90 (13.8)

Total 22 (100.0) 630 (100.0) 652 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.
57

Table 74. Midblock pedestrian crashes on Ohio State highways by hour of the day

(2005–2009).

Hour of day

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

12–12:59 a.m. 2 (9.1) 15 (2.4) 17 (2.6)

1–1:59 a.m. 1 (4.5) 25 (4.0) 26 (4.0)

2–2:59 a.m. 0 (0.0) 30 (4.8) 30 (4.6)

3–3:59 a.m. 0 (0.0) 12 (1.9) 12 (1.8)

4–4:59 a.m. 0 (0.0) 3 (0.5) 3 (0.5)

5–5:59 a.m. 1 (4.5) 9 (1.4) 10 (1.5)

6–6:59 a.m. 2 (9.1) 15 (2.4) 17 (2.6)

7–7:59 a.m. 1 (4.5) 20 (3.2) 21 (3.2)

8–8:59 a.m. 3 (13.6) 11 (1.7) 14 (2.1)

9–9:59 a.m. 0 (0.0) 10 (1.6) 10 (1.5)

10–10:59 a.m. 3 (13.6) 16 (2.5) 19 (2.9)

11–11:59 a.m. 0 (0.0) 21 (3.3) 21 (3.2)

Noon–12:59 p.m. 2 (9.1) 19 (3.0) 21 (3.2)

1–1:59 p.m. 0 (0.0) 22 (3.5) 22 (3.4)

2–2:59 p.m. 2 (9.1) 43 (6.8) 45 (6.9)

3–3:59 p.m. 2 (9.1) 45 (7.1) 47 (7.2)

4–4:59 p.m. 0 (0.0) 43 (6.8) 43 (6.6)

5–5:59 p.m. 1 (4.5) 50 (7.9) 51 (7.8)

6–6:59 p.m. 2 (9.1) 49 (7.8) 51 (7.8)

7–7:59 p.m. 0 (0.0) 48 (7.6) 48 (7.4)

8–8:59 p.m. 0 (0.0) 37 (5.9) 37 (5.7)

9–9:59 p.m. 0 (0.0) 37 (5.9) 37 (5.7)

10–10:59 p.m. 0 (0.0) 27 (4.3) 27 (4.1)

11–11:59 p.m. 0 (0.0) 23 (3.7) 23 (3.5)

Unknown 0 — 0 — 0 —

Total 22 (100.0) 630 (100.0) 652 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Texas

Data for Texas include crash data submitted to the department that can include both on-State and 

off-State roadways. Data analyzed for this report include the years from 2003 to 2009, inclusive.

Table 75 shows that during the study period, 3,134,365 crashes occurred on the Texas roadway

system. Of these, 39,993 (1.3 percent) were pedestrian crashes. Nearly 50 percent of the 

pedestrian crashes occurred at non-intersection locations (defined as midblock locations for this 

report). Only 136 crashes (0.7 percent of the midblock crashes) were classified as occurring at 

midblock crosswalks.

Table 76 shows the relative frequencies of Texas crashes by midblock crosswalk and other 

midblock roadway locations. Table 77 classifies midblock pedestrian crashes in Texas by crash 

severity level.
58

Table 75. Summary of pedestrian crash data for Texas roadways (2003–2009).

Crashes

Number of crashes by year

2003 2004 2005 2006 2007 2008 2009 Combined

Total crashes 459,725 447,037 463,830 437,290 458,289 439,527 428,667 3,134,365

Pedestrian crashes 5,998 5,444 5,620 5,146 5,751 6,244 5,790 39,993

Pedestrian midblock 

crashesa 3,042 2,741 2,803 2,392 2,662 2,850 2,493 18,983

Pedestrian midblock 

roadway crashesb 3,042 2,741 2,803 2,354 2,628 2,811 2,468 18,847

Pedestrian midblock 

crossing crashes 0 0 0 38 34 39 25 136 a

Crashes at nonintersection and in-roadway along with pedestrian as the code for harmful event, person type, or vehicle unit 

description. b

Midblock roadway crashes not coded with “crosswalk” as the traffic control device present.

Table 76. Midblock pedestrian crashes on Texas roadways by pedestrian location

(2003–2009).

Year

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

2003 0 (0.0) 3,042 (100.0) 3,042

2004 0 (0.0) 2,741 (100.0) 2,741

2005 0 (0.0) 2,803 (100.0) 2,803

2006 38 (1.6) 2,354 (98.4) 2,392

2007 34 (1.3) 2,628 (98.7) 2,662

2008 39 (1.4) 2,811 (98.6) 2,850

2009 25 (1.0) 2,468 (99.0) 2,493

Total 136 (0.7) 18,847 (99.3) 18,983

Percentages in this table are row percentages.

Table 77. Midblock pedestrian crashes on Texas roadways by injury severity (2003–2009).

Injury severity

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Incapacitating injury 29 (21.5) 4,111 (22.0) 4,140 (22.0)

Non-incapacitating injury 48 (35.6) 6,799 (36.3) 6,847 (36.3)

Possible injury 48 (35.6) 4,875 (26.1) 4,923 (26.1)

Fatal 6 (4.4) 2,192 (11.7) 2,198 (11.7)

Not injured 4 (3.0) 734 (3.9) 738 (3.9)

Unknown 1 — 136 — 137 —

Total 136 (100.0) 18,847 (100.0) 18,983 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 78 presents a classification of midblock pedestrian crashes in Texas by the traffic control 

devices present. Table 79 through table 83 present Texas data for pedestrian crashes at midblock 

locations by weather condition, road surface condition, light condition, hour of the day, and day 

of the week.
59

Table 78. Midblock pedestrian crashes on Texas roadways by traffic control device

(2003–2009).

Traffic control device

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

No control or inoperative 0 (0.0) 3,770 (21.0) 3,770 (20.9)

None 0 (0.0) 5,696 (31.8) 5,696 (31.5)

Inoperative 0 (0.0) 7 (0.0) 7 (0.0)

Officer 0 (0.0) 52 (0.3) 52 (0.3)

Flagman 0 (0.0) 45 (0.3) 45 (0.2)

Signal light 0 (0.0) 648 (3.6) 648 (3.6)

Flashing red light 0 (0.0) 21 (0.1) 21 (0.1)

Flashing yellow light 0 (0.0) 10 (0.1) 10 (0.1)

Stop sign 0 (0.0) 262 (1.5) 262 (1.4)

Yield sign 0 (0.0) 54 (0.3) 54 (0.3)

Warning sign 0 (0.0) 85 (0.5) 85 (0.5)

Center stripe/divider 0 (0.0) 5,609 (31.3) 5,609 (31.0)

No passing zone 0 (0.0) 356 (2.0) 356 (2.0)

RR gate/signal 0 (0.0) 8 (0.0) 8 (0.0)

School zone 0 (0.0) 62 (0.3) 62 (0.3)

Crosswalk 136 (100.0) 0 (0.0) 136 (0.8)

Bike lane 0 (0.0) 5 (0.0) 5 (0.0)

Other 0 (0.0) 1,145 (6.4) 1,145 (6.3)

Officer or flagman 0 (0.0) 76 (0.4) 76 (0.4)

Turn marks 0 (0.0) 29 (0.2) 29 (0.2)

Blank 0 — 907 — 907 —

Total 136 (100.0) 18,847 (100.0) 18,983 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 79. Midblock pedestrian crashes on Texas roadways by weather condition

(2003–2009).

Weather condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Clear/cloudy 122 (92.4) 17,466 (94.2) 17,588 (94.2)

Rain 10 (7.6) 872 (4.7) 882 (4.7)

Sleet/hail 0 (0.0) 20 (0.1) 20 (0.1)

Snow 0 (0.0) 15 (0.1) 15 (0.1)

Fog 0 (0.0) 86 (0.5) 86 (0.5)

Blowing sand/snow 0 (0.0) 8 (0.0) 7 (0.0)

Severe crosswinds 0 (0.0) 14 (0.1) 14 (0.1)

Blowing dust 0 (0.0) 2 (0.0) 2 (0.0)

Other 0 (0.0) 52 (0.3) 52 (0.3)

Blank 4 — 281 — 285 —

Unknown 0 — 32 — 32 —

Total 136 (100.0) 18,847 (100.0) 18,983 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
60

Table 80. Midblock pedestrian crashes on Texas roadways by road surface condition

(2003–2009).

Road surface condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Dry 119 (89.5) 17,073 (91.2) 17,192 (91.2)

Wet 13 (9.8) 1,505 (8.0) 1,518 (8.1)

Standing water 0 (0.0) 8 (0.0) 8 (0.0)

Snowy/icy 0 (0.0) 20 (0.1) 20 (0.1)

Slush 0 (0.0) 3 (0.0) 3 (0.0)

Ice 1 (0.8) 31 (0.2) 32 (0.2)

Muddy 0 (0.0) 4 (0.0) 4 (0.0)

Snow 0 (0.0) 7 (0.0) 7 (0.0)

Sand (mud, dirt) 0 (0.0) 26 (0.1) 26 (0.1)

Other 0 (0.0) 36 (0.2) 36 (0.2)

Unknown 0 — 33 — 33 —

Blank 3 — 101 — 104 —

Total 136 (100.0) 18,847 (100.0) 18,983 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 81. Midblock pedestrian crashes on Texas roadways by light condition (2003–2009).

Light condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Daylight 100 (75.2) 9,364 (50.7) 9,464 (50.9)

Dawn 2 (1.5) 191 (1.0) 193 (1.0)

Dark (not lighted) 5 (3.8) 3,893 (21.1) 3,898 (21.0)

Dark (lighted) 26 (19.5) 4,419 (23.9) 4,445 (23.9)

Dusk 0 (0.0) 411 (2.2) 411 (2.2)

Dark (unknown lighting) 0 (0.0) 168 (0.9) 168 (0.9)

Other 0 (0.0) 8 (0.0) 8 (0.0)

Unknown 0 — 27 — 27 —

Blank 3 — 366 — 369 —

Total 136 (100.0) 18,847 (100.0) 18,983 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
61

Table 82. Midblock pedestrian crashes on Texas roadways by hour of day (2003–2009).

Hour of day

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

12–12:59 a.m. 0 (0.0) 20 (0.1) 20 (0.1)

1–1:59 a.m. 1 (0.7) 496 (2.6) 497 (2.6)

2–2:59 a.m. 2 (1.5) 638 (3.4) 640 (3.4)

3–3:59 a.m. 0 (0.0) 288 (1.5) 288 (1.5)

4–4:59 a.m. 1 (0.7) 203 (1.1) 204 (1.1)

5–5:59 a.m. 3 (2.2) 260 (1.4) 263 (1.4)

6–6:59 a.m. 3 (2.2) 449 (2.4) 452 (2.4)

7–7:59 a.m. 16 (11.8) 653 (3.5) 669 (3.5)

8–8:59 a.m. 13 (9.6) 407 (2.2) 420 (2.2)

9–9:59 a.m. 9 (6.6) 358 (1.9) 367 (1.9)

10–10:59 a.m. 4 (2.9) 423 (2.3) 427 (2.9)

11–11:59 a.m. 6 (4.4) 516 (2.7) 522 (2.8)

Noon–12:59 p.m. 7 (5.1) 1,194 (6.4) 1,201 (6.3)

1–1:59 p.m. 1 (0.7) 623 (3.3) 624 (3.3)

2–2:59 p.m. 13 (9.6) 793 (4.2) 806 (4.3)

3–3:59 p.m. 8 (5.9) 1,179 (6.3) 1,187 (6.3)

4–4:59 p.m. 11 (8.1) 1,252 (6.7) 1,263 (6.7)

5–5:59 p.m. 12 (8.8) 1,400 (7.5) 1,412 (7.5)

6–6:59 p.m. 7 (5.1) 1,680 (8.9) 1,687 (8.9)

7–7:59 p.m. 9 (6.6) 1,539 (8.2) 1,548 (8.2)

8–8:59 p.m. 3 (2.2) 1,388 (7.4) 1,391 (7.4)

9–9:59 p.m. 3 (2.2) 1,330 (7.1) 1,333 (7.0)

10–10:59 p.m. 3 (2.2) 988 (5.3) 991 (5.2)

11–11:59 p.m. 1 (0.7) 707 (3.8) 708 (3.7)

Unknown 0 — 63 — 63 —

Total 136 (100.0) 18,847 (100.0) 18,983 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 83. Midblock pedestrian crashes on Texas roadway by day of week (2003–2009).

Weekday

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Sunday 13 (9.6) 2,814 (14.9) 2,827 (14.9)

Monday 29 (21.3) 2,370 (12.6) 2,399 (12.6)

Tuesday 21 (15.4) 2,317 (12.3) 2,338 (12.3)

Wednesday 25 (18.4) 2,344 (12.4) 2,369 (12.5)

Thursday 22 (16.2) 2,493 (13.2) 2,515 (13.2)

Friday 18 (13.2) 3,150 (16.7) 3,168 (16.7)

Saturday 8 (5.9) 3,359 (17.8) 3,367 (17.7)

Total 136 (100.0) 18,847 (100.0) 18,983 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Washington

HSIS data for Washington include crash data only for the state highway system, consisting of 

approximately 7,193 mi of highways. While some city streets and many small town “main 

streets” and suburban arterials are State highways, many areas with the highest concentrations of 
62

pedestrians in Washington are not on the State highway system. Data analyzed for this report 

include the years from 2005 to 2008, inclusive.

Table 84 shows that during the study period, 188,992 crashes occurred on the Washington State 

highway system. Of these, 1,573 (0.8 percent) were pedestrian crashes. Nearly 40 percent of the 

pedestrian crashes occurred at midblock locations. Only 29 crashes (5.0 percent of the midblock 

crashes) were classified as occurring at midblock crosswalks.

Table 84. Summary of pedestrian crash data for Washington State highways (2005–2008).

Crashes

Number of crashes by year

2005 2006 2007 2008 Combined

Total crashes 48,023 48,985 47,732 44,252 188,992

Pedestrian crashes 359 400 410 404 1,573

Pedestrian midblock crashes 159 161 147 152 619

Pedestrian midblock roadway crashes 150 155 141 144 590

Pedestrian midblock crossing crashes 9 6 6 8 29

Table 85 shows the relative frequencies of Washington crashes by midblock crosswalk and other 

midblock roadway locations. 

Table 85. Midblock pedestrian crashes on Washington State highways by pedestrian 

location (2005–2008).

Year

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

2005 9 (4.2) 150 (95.8) 159

2006 6 (0.8) 155 (99.2) 161

2007 6 (5.3) 141 (94.7) 147

2008 8 (2.6) 144 (97.4) 152

Total 29 (3.7) 590 (96.3) 619

Percentages in this table are row percentages.

Table 86 classifies midblock pedestrian crashes in Washington by crash severity level. Table 87

classified midblock pedestrian crashes in Washington by contributing factors, many of which are 

related to pedestrian actions.

Table 88 through table 92 present Washington data for pedestrian crashes at midblock locations

by presence of median, road surface condition, light condition, hour of the day, and day of the 

week.
63

Table 86. Midblock pedestrian crashes on Washington State highways by injury severity

(2005–2008).

Injury severity

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Fatal 0 (0.0) 71 (12.1) 71 (11.5)

Incapacitating injury 4 (13.8) 148 (25.2) 152 (24.6)

Non-incapacitating injury 10 (34.5) 189 (32.1) 199 (32.3)

Possible injury 12 (41.4) 139 (23.6) 151 (24.5)

Property damage only 3 (10.3) 41 (7.0) 44 (7.1)

Injury unknown 0 — 2 — 2 —

Total 29 (100.0) 590 (100.0) 619 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 87. Midblock pedestrian crashes on Washington State highways by contributing 

factor (2005–2008).

Contributing factor

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Under the influence of alcohol or drugs 0 (0.0) 39 (6.8) 39 (6.5)

Speeding 0 (0.0) 44 (7.7) 44 (7.3)

Pedestrian did not yield to vehicle 0 (0.0) 2 (0.3) 2 (0.3)

Improper passing 1 (3.4) 3 (0.5) 4 (0.7)

Following too closely 0 (0.0) 4 (0.7) 4 (0.7)

Failing to signal 0 (0.0) 1 (0.2) 1 (0.2)

Apparently asleep, ill, or fatigued 0 (0.0) 4 (0.7) 4 (0.7)

Operating defective equipment 0 (0.0) 7 (1.2) 7 (1.2)

Improper U-turn 0 (0.0) 2 (0.3) 2 (0.3)

Motorist did not yield to pedestrian 16 (55.3) 89 (15.6) 105 (17.4)

Inattention 1 (3.4) 13 (2.3) 14 (2.3)

Improper backing 0 (0.0) 7 (1.2) 7 (1.2)

Disregard flagger/officer 0 (0.0) 8 (1.4) 8 (1.3)

Failure to use crosswalk 0 (0.0) 1 (0.2) 1 (0.2)

Other 2 (6.9) 97 (16.9) 99 (16.4)

None 9 (31.0) 252 (44.0) 261 (43.3)

Unknown 0 — 17 — 17 —

Total 29 (100.0) 590 (100.0) 619 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 88. Midblock pedestrian crashes on Washington State highways by presence of 

median (2005–2008).

Presence of median

Number (percent) of crashes by presence of median

Midblock crosswalk Midblock roadway Combined

Divided 5 (19.2) 183 (33.5) 188 (32.9)

Undivided 21 (80.8) 363 (66.5) 384 (67.1)

Unknown 3 — 44 — 47 —

Total 29 (100.0) 590 (100.0) 619 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.
64

Table 89. Midblock pedestrian crashes on Washington State highways by road surface 

condition (2005–2008).

Road surface condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Dry 21 (72.4) 412 (70.5) 433 (70.6)

Wet 7 (24.1) 135 (23.1) 142 (23.2)

Snow/slush 1 (3.4) 22 (3.8) 23 (3.8)

Ice 0 (0.0) 12 (2.1) 12 (2.0)

Sand/mud/dirt 0 (0.0) 1 (0.2) 1 (0.2)

Other 0 (0.0) 2 (0.3) 2 (0.3)

Unknown 0 — 6 — 6 —

Total 29 (100.0) 590 (100.0) 619 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 90. Midblock pedestrian crashes on Washington State highways by light condition 

(2005–2008).

Light condition

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Daylight 19 (65.6) 266 (45.5) 285 (46.4)

Dusk-dawn 2 (6.9) 23 (3.9) 25 (4.0)

Dark-street lights on 6 (20.7) 176 (30.0) 182 (29.6)

Dark-no street lights 2 (6.8) 120 (20.4) 122 (19.8)

Other 0 (0.0) 1 (0.2) 1 (0.2)

Unknown 0 — 4 — 4 —

Total 29 (100.0) 590 (100.0) 619 (100.0)

— Indicates count not included in calculation of percentage.

Column percentages may not appear to add to 100 percent due to rounding.

Table 91. Midblock pedestrian crashes on Washington State highways by day of the week 

(2005–2008).

Day of week

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

Sunday 0 (0.0) 78 (13.2) 78 (12.6)

Monday 4 (13.8) 79 (13.4) 83 (13.4)

Tuesday 5 (17.2) 67 (11.4) 72 (11.6)

Wednesday 5 (17.2) 77 (13.1) 82 (13.2)

Thursday 7 (24.1) 81 (13.7) 88 (14.2)

Friday 5 (17.2) 105 (17.8) 110 (17.8)

Saturday 3 (10.3) 103 (17.5) 106 (17.1)

Total 29 (100.0) 590 (100.0) 619 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.
65

Table 92. Midblock pedestrian crashes on Washington State highways by hour of the day 

(2005–2008).

Hour of day

Number (percent) of crashes by pedestrian location

Midblock crosswalk Midblock roadway Combined

12–12:59 am 0 (0.0) 22 (3.7) 22 (3.6)

1–1:59 am 1 (3.4) 18 (3.1) 19 (3.1)

2–2:59 am 0 (0.0) 22 (3.7) 22 (3.6)

3–3:59 am 1 (3.4) 8 (1.4) 9 (1.5)

4–4:59 am 0 (0.0) 13 (2.2) 13 (2.1)

5–5:59 am 0 (0.0) 13 (2.2) 13 (2.1)

6–6:59 am 1 (3.4) 13 (2.2) 14 (2.3)

7–7:59 am 1 (3.4) 16 (2.7) 17 (2.7)

8–8:59 am 1 (3.4) 18 (3.1) 19 (3.1)

9–9:59 am 0 (0.0) 17 (2.9) 17 (2.7)

10–10:59 am 0 (0.0) 11 (1.9) 11 (1.8)

11–11:59 am 2 (6.9) 23 (3.9) 25 (4.0)

Noon–12:59 pm 1 (3.4) 26 (4.4) 27 (4.4)

1–1:59 pm 2 (6.9) 21 (3.6) 23 (3.7)

2–2:59 pm 2 (6.9) 33 (5.6) 35 (5.7)

3–3:59 pm 4 (13.8) 30 (5.1) 34 (5.5)

4–4:59 pm 3 (10.3) 25 (4.2) 28 (4.5)

5–5:59 pm 5 (17.2) 51 (8.6) 56 (9.0)

6–6:59 pm 1 (3.4) 39 (6.6) 40 (6.5)

7–7:59 pm 3 (10.3) 38 (6.4) 41 (6.6)

8–8:59 pm 1 (3.4) 32 (5.4) 33 (5.3)

9–9:59 pm 0 (0.0) 32 (5.4) 32 (5.2)

10–10:59 pm 0 (0.0) 49 (8.3) 49 (7.9)

11–11:59 pm 0 (0.0) 20 (3.4) 20 (3.2)

Total 29 (100.0) 590 (100.0) 619 (100.0)

Column percentages may not appear to add to 100 percent due to rounding.

67

CHAPTER 4. LOCAL FIELD OBSERVATIONS

This chapter documents the results of field observations at midblock pedestrian crossings. The 

purpose of these observations was to serve as a first step toward deciding the types of treatments 

and locations to be studied in the research.

OBSERVATIONS OF MIDBLOCK PEDESTRIAN CROSSINGS 

The research team made observations at selected midblock pedestrian crossings with a range of 

traffic control treatments. Ten midblock pedestrian crossings in 5 States were observed, 

including sites in eight different cities. These observations were not intended to be a structured 

evaluation of these particular crossings; rather they were merely intended as a source of ideas 

about how particular crossing types could potentially be evaluated later in the study. These 

observations were generally made in the field during the summer of 2011. The crossings 

observed were not selected as candidates for evaluation; indeed, many of the observed locations 

have already been treated in particular ways. The observation periods were typically brief (15 to 

30 min), and insights and assessments gained from these observations, by intention, should be 

regarded as anecdotal rather than definitive. Development of evaluation approaches for specific 

crossing treatments, which had not yet been selected, were done later in the research.

The midblock pedestrian crossings observed were at the following locations:

• E. Bidwell Avenue between Riley Avenue and Coloma Avenue in Folsom, CA.

• N. El Dorado Avenue between E. Market Street and E. Weber Street in Stockton, CA.

• N. El Dorado Avenue between E. Weber Street and E. Miner Street in Stockton, CA.

• W. 80th Street between Overland Park Drive and Marty Street in Overland Park, KS.

• W. 39th Avenue between Rainbow Boulevard and Cambridge Street in 

Kansas City, KS.

• Oak Street between 51st Street and 52nd Street in Kansas City, MO.

• Rockhill Road between 50th Street and 51st Street in Kansas City, MO.

• W. Walnut Street at N. Bullock Drive in Garland, TX.

• Barton Springs Road between South 1st Street and Bouldin Avenue in Austin, TX.

• 23rd Street near Crystal City Mall in Arlington, VA.

The observations of the crossings are discussed in the following subsections.
68

Crosswalk 1: E. Bidwell Avenue Between Riley Avenue and Coloma Avenue in Folsom, CA

Crosswalk 1 is a midblock crossing that adjoins Folsom Lakes High School and Sutter Middle 

School. There is a marked unsignalized pedestrian crossing with two fixed upright “State LawYield to Pedestrian” paddles near the center of the road. Pedestrian pushbuttons activate flashing 

lights embedded in the pavement surface immediately in advance of the crosswalk. The street 

crossed has one lane in each direction of travel plus a center TWLTL. Figure 5 is a photograph

of this crossing. Figure 6 shows the pedestrian view of the crosswalk, and figure 7 shows the 

pedestrian pushbutton.

Figure 5. Photo. Unsignalized pedestrian crosswalk in Folsom, CA.

Figure 6. Photo. Unsignalized pedestrian crosswalk in Folsom, CA, pedestrian view.
69

Figure 7. Photo. Unsignalized pedestrian crosswalk in Folsom, CA, pedestrian pushbutton.

Two crossing maneuvers by school-age pedestrians were observed, one by a single pedestrian

and one by a group of approximately 30 students accompanied by their teacher. The pedestrian 

pushbutton was pressed to activate the in-pavement flashing lights prior to both crossing 

maneuvers. Based on limited observation, the traffic control, including the in-pavement flashing 

lights, was only partially effective in producing driver compliance with the legal requirement to 

yield to pedestrians. Prior to both crossing maneuvers, two vehicles that should have yielded to 

the pedestrian(s) proceeded through the crosswalk without stopping, while the third vehicle to 

arrive stopped in advance of the crosswalk and allowed the pedestrian(s) to cross. No vehicle–

pedestrian conflicts were observed because the pedestrians did not leave the curb until a vehicle 

stopped, but the pedestrians effectively yielded the right of way to the first two vehicles to arrive 

at the crosswalk, rather than vice versa.

All observed pedestrian crossing maneuvers were made within the marked crosswalk. The 

pedestrian pushbutton was located such that pedestrians who activated the pushbutton were 

likely to cross within the crosswalk.

Crosswalk 2: N. El Dorado Street Between E. Market Street and E. Weber Street in 

Stockton, CA 

Crosswalk 2 is a midblock crossing located in downtown Stockton. There is a marked 

unsignalized crosswalk with pedestrian crosswalk signs (W11-2). Pedestrian pushbuttons 

activate flashing lights mounted on the pedestrian crosswalk signs and embedded in the 

pavement surface in advance of the crosswalk. The pushbuttons also activate an audible message 

to pedestrians: “Cross street with caution. Vehicles may not stop.” The street crossed is one-way 

northbound with three travel lanes and metered curb parking on the east side of the street. The 

crossing connects pedestrian malls on each side of the street that extend through the adjacent 

blocks. Figure 8 is a photograph of this crossing. Figure 9 shows the pedestrian view of the 

crosswalk, and figure 10 shows the pedestrian pushbutton.

Even during an early evening observation period, there was some pedestrian activity at this 

location. Two pedestrian crossing maneuvers were observed. In both cases, the pedestrian used 

the pushbutton to activate the flashing lights, and all approaching traffic immediately stopped. 

All observed pedestrian crossing maneuvers were made within the marked crosswalk. 
70

The pedestrian pushbutton was located such that pedestrians who activated the pushbutton were 

likely to cross within the crosswalk.

Figure 8. Photo. Unsignalized pedestrian crosswalk in Stockton, CA.

Figure 9. Photo. Unsignalized pedestrian crosswalk in Stockton, CA, pedestrian view.

Figure 10. Photo. Unsignalized pedestrian crosswalk in Stockton, CA, pedestrian 

pushbutton.
71

Crosswalk 3: N. El Dorado Street Between E. Market Street and E. Miner Street in 

Stockton, CA 

Crosswalk 3 is a midblock crossing located in downtown Stockton. There is a marked 

unsignalized crosswalk with pedestrian crosswalk signs (W11-2). Pedestrian pushbuttons 

activate flashing lights mounted on the pedestrian crosswalk signs and embedded in the 

pavement surface in advance of the crosswalk. The pushbuttons also activate an audible message 

to pedestrians: “Crosswalk system has been activated. Vehicles may not stop.” The street crossed 

is one-way northbound with three travel lanes and metered curb parking on the east side of the 

street. The crossing connects a pedestrian plaza and parking area on the west side of the street 

with an entertainment center, including a movie theatre and several restaurants, on the east side 

of the street. This crossing is one block north of the preceding crossing. Figure 11 is a 

photograph of this crossing. 

During the early evening hours, there was moderate pedestrian activity at this location. All 

observed pedestrians used the pushbutton to activate the flashing lights and, in all cases, the 

approaching traffic immediately stopped. All observed pedestrian crossing maneuvers were 

made within the marked crosswalk. The pedestrian pushbutton was located such that pedestrians 

who activated the pushbutton were likely to cross within the crosswalk.

Several other midblock crossings were noted in downtown Stockton, but the other locations 

appeared likely to have pedestrian crossing activity only during normal work hours.

Figure 11. Photo. Unsignalized pedestrian crosswalk in Stockton, CA.
72

Crosswalk 4: W. 80th Street Between Overland Park Drive and Marty Street in Overland 

Park, KS

Crosswalk 4 is a midblock crossing located in downtown Overland Park, KS. There is a brick 

textured marked unsignalized crosswalk with pedestrian crosswalk signs (W11-2). Curb 

extensions have been implemented at this crosswalk to shorten the crossing distance. The street 

crossed is a two-way street with two travel lanes and curb parking on both sides of the street. The 

crossing connects restaurants and shops on either side of the street. Figure 12 is a photograph of 

this crossing. 

Figure 12. Photo. Unsignalized pedestrian crosswalk in Overland Park, KS.

Three midblock crossing maneuvers were observed, but only one of them was made in the 

crosswalk. This may be because of the low traffic volumes at the time the observation was 

conducted.

Crosswalk 5: W. 39th Avenue Between Rainbow Blvd and Cambridge Street in Kansas 

City, KS 

Crosswalk 5 is a midblock crossing located on the campus of the University of Kansas Medical 

Center. There is a marked unsignalized crosswalk with pedestrian crosswalk signs (W11-2 and 

K-2025). Yellow flashers alert approaching motorists of the crosswalk. The street crossed is a 

two-way street with two travel lanes in each direction and a raised median. The crosswalk cuts 

through the raised median. The crossing connects a large parking lot to the medical center. 

Figure 13 is a photograph of the crossing. 
73

Figure 13. Photo. Unsignalized pedestrian crosswalk in Kansas City, KS.

Several pedestrian crossing maneuvers were observed. All observed pedestrian crossing 

maneuvers were made within the marked crosswalk, and the approaching traffic immediately 

stopped in all cases.

Crosswalk 6: Oak Street Between 51st Street and 52nd Street in Kansas City, MO

Crosswalk 6 is a midblock crossing located near the campus of the University of Missouri—

Kansas City (UMKC). There is a marked signalized crosswalk with pedestrian crosswalk signs 

(W11-2). Pedestrian pushbuttons activate the signal. The street crossed is two-way street with 

two travel lanes, a TWLTL, and limited parking on the west side of the street. The crossing 

connects a large parking area on the west side of the street with the UMKC campus. Figure 14

and figure 15 are photographs of this crossing. 

Several pedestrian crossing maneuvers were observed. All observed pedestrian crossing 

maneuvers were made within the marked crosswalk. However, not all were made during the red 

signal phase; some were made during the green and yellow phases of the signal.
74

Figure 14. Photo. Signalized pedestrian crosswalk in Kansas City, MO.

Figure 15. Photo. Another view of signalized pedestrian crosswalk in Kansas City, MO.

Crosswalk 7: Rockhill Road Between 50th Street and 51st Street in Kansas City, MO 

Crosswalk 7 is a midblock crossing located near the UMKC campus. There is an unmarked 

unsignalized crosswalk with pedestrian crosswalk signs (W11-2) and overhead yellow flashers

that flash continuously. The street crossed is a two-way street with two travel lanes in each 

direction. The crossing connects a parking area on the west side of the street with UMKC 

campus buildings. Figure 16 is a photograph of this crossing. No pedestrian crossings were 

observed at this crosswalk.
75

Figure 16. Photo. Unsignalized pedestrian crosswalk in Kansas City, MO.

Crosswalk 8: W. Walnut Street at N. Bullock Drive in Garland, TX

Crosswalk 8 is located in Garland, TX. It is part of the school route to Bullock Elementary 

School and connects the Harris Hollabaugh Park and Community Center on the north side of 

Walnut Street with the residences and school on the south side. The school is located about 

1,000 ft south of Walnut Street on Bullock. Walnut is a four-lane arterial with a center TWLTL

and a posted speed limit of 35 mi/h. The subject crosswalk was recently improved as part of the 

development that opened the community center. Curb ramps were added at either end of the 

crosswalk where none previously existed, new fluorescent yellow-green school crosswalk 

warning assembly signs (S1-1 and W16-7L) were posted on either end of the crosswalk, and a 

pole-and-mast arm assembly was installed on the south end of the crosswalk, to enable the future 

installation of an overhead RRFB. The school crosswalk warning assembly sign for eastbound 

traffic is mounted on the pole. A crossing guard is stationed at the crosswalk during most of the 

periods during which the reduced speed limit is in place, approximately 6:55 to 8:00 a.m. and 

2:55 to 3:45 p.m. Figure 17 is a photograph of the crossing.

Onsite observations indicated that yielding at this location was very low during periods in which 

the crossing guard was not present. In fact, researchers observed almost no vehicles yielding to 

pedestrians during any non-school period. However, yielding was nearly 100 percent when the 

crossing guard facilitated crossings. Researchers observed 3 pedestrians using the crosswalk 

during nonschool zone periods and 13 crossings that occurred away from the crosswalk during 

the observation period. 
76

Figure 17. Photo. A different view of the crosswalk in Garland, TX.

Crosswalk 9: Barton Springs Road Between South 1st Street and Bouldin Avenue in 

Austin, TX

Crosswalk 9 connects the Austin Energy office building on the south side of Barton Springs 

Road with the parking garage attached to the Palmer Events Center on the north. The crosswalk 

has ladder markings and had previously employed in-pavement pedestrian-activated flashing 

lights adjacent to the crosswalk. Those lights have been deactivated, and an overhead pedestrian 

hybrid beacon has been installed for each direction of vehicle traffic. Although there is a narrow 

median refuge area present, both directions of traffic are stopped when the beacon is activated, 

and the WALK signal is provided for the entire width of the roadway. Figure 18 is a photograph 

of this crossing.
77

Figure 18. Photo. Crosswalk on Barton Springs Road in Austin, TX.

Onsite observations during a portion of an afternoon peak period suggested that vehicle 

compliance was good, with no driver observed running the steady red signal. Researchers also 

observed that drivers’ response to the final flashing red phase of the beacon was mixed. Some 

drivers recognized that the “wig-wag” red display allowed them to proceed after stopping if no 

pedestrians were present, while other drivers continued to wait at the stop line until the beacon 

went dark. The stop line for eastbound traffic is an advance stop line, located upstream of the 

nose of the median island; although all of the observed drivers stopped prior to the crosswalk, 

compliance with the stop line in the eastbound direction was not as high as that in the westbound 

direction.

Crosswalk 10: 23rd Street Near Crystal City Mall in Arlington, VA

A crosswalk on 23rd Street between Crystal City Mall and office buildings was observed in 

Arlington, VA, following the kickoff meeting on October 20, 2010. This midblock crossing had 

continental pavement markings and a pedestrian crossing sign. In this region, 23rd Street is a 

divided roadway with a wide brick median with trees and benches. The crosswalks for each side 

of the street are in alignment with entrances to the mall or to an office building. Because of the 

split, several of the crossing pedestrians were observed to be walking outside the crosswalk 

markings (see figure 19). The pedestrians tended to walk a straight (shortest distance) path 

between the buildings rather than follow the crosswalk markings. Good yielding behavior by the 

drivers was observed.
78

An interesting feature that was present during this observation period was temporary markings 

that had the appearance of a colorful crossing. These markings, shown in figure 20, were located 

on Crystal Drive, which intersects 23rd Street. Later investigation revealed that it was pavement 

tattooing used as a promotion for the area.

Figure 19. Photo. Example of pedestrians outside of crosswalk at site on 23rd Street.

Figure 20. Photo. Colored pavement markings used to promote local area.
79

ASSESSMENT OF RESULTS

The limited field observations described above indicated the following:

• Field studies of driver compliance with laws requiring drivers to yield to pedestrians 

and the location of pedestrian crossing maneuvers (within marked crosswalk, partially 

within marked crosswalk, outside of marked crosswalk) can be readily conducted at a 

variety of pedestrian crossing types.

• The inclusion of flashing lights on pedestrian crosswalk signs (such as at the 

Stockton, CA, sites) rather than just in the pavement surface (such as at the Folsom, 

CA, site) appeared to substantially increase driver compliance with the law requiring 

yielding to pedestrians. (However, no nighttime observations were made of the inpavement flasher treatment by itself; given that this installation was at a school 

crossing, such observation would not have been relevant.)

• School area locations such as the ones in Folsom, CA, and Garland, TX, can be 

effectively studied only at selected times of day when students are arriving at and 

departing from school or after-school activities.

81

CHAPTER 5. CLOSED-COURSE STUDY

OVERVIEW

Pedestrian crossing traffic control options include numerous combinations of signs and flashing 

beacons/LEDs. The breadth of combinations gives rise to a number of design variables; the 

optimal combination of these variables that results in long-term driver yielding to pedestrians 

(rather than just a “honeymoon phase” of compliance) has not been established. Several design 

factors have not been fully researched to determine the optimal design, including the following:

• Flash rate of beacons or LEDs.

• Shape and size of beacons/LEDs (round, rectangular, 8-inch, 12-inch, 5- by 2-inch, 

etc.).

• Placement of beacons or LEDs in the assembly units (within the sign, top, sides, 

bottom, overhead, in-road, etc.)

• Placement and message of signs and markings (warning and/or regulatory) for 

uncontrolled crossings (with and without beacons/LEDs).

• Brightness and how it influences driver behavior and how it affects glare and message 

recognition (especially given various lighting conditions such as day, night, fog, rain, 

snow).

• Influence of vehicle speed.

This research endeavors to provide the analytical basis for addressing several of these variables. 

Although it will not address every variable, the findings build a foundation and future research 

can fill the remaining gaps.

Closed-Course Study Objective

There are several questions regarding the optimal configuration of beacons used with Pedestrian 

Crossing signs (abbreviated as “Ped X-ing” in tables and figures in this document). Specific 

research objectives for this closed-course study are the following:

• Determine whether the shape, size, and placement of flashing beacon/LEDs affects 

the following: 

o Sign legibility distances (legibility distance is the location upstream of a sign 

where a driver can correctly read all of the words on a sign or correctly 

identify the symbol on a symbol sign).

o Object detection. 

• Determine driver ratings of disability glare for 8-inch circular beacons and LEDembedded signs using a rapid flash pattern. 
82

• Identify up to two assemblies for field evaluation to be conducted following the 

conclusion of the closed-course tasks.

Study Approach Overview

As part of this FHWA study, participants drove a Texas A&M Transportation Institute (TTI)

vehicle (a 2009 Ford Explorer) on a closed course at the Texas A&M University Riverside 

Campus. During each lap, the participants viewed 8 study assemblies, 9 distractor signs, and up 

to 11 objects. Each participant drove the course twice, with a pause between laps for the field 

crew to switch the signs and objects for the second lap. Lessons learned from a previous study 

demonstrated that the position of traffic control devices on the course could affect the detection 

distance.(25) Therefore, researchers rotated the assemblies during this study. The rotation was 

done when approximately 25, 50, and 75 percent of the participants had completed the study. 

Figure 21 shows the driving study course, along with the positions of the signs and objects (when 

present). Based on the available budget, the goal was to have 64 participants in the study; 

therefore, researchers planned rotations to occur after 16 participants drove the course.
83

Figure 21. Diagram. Test route for Riverside study along with signs and objects positions.

The tasks for the participants while driving the route were to indicate when they could first do 

the following:

• See warning lights.

• See road signs.

• Read the words or identify the symbol on the road signs.

• See objects (pedestrian, trash can, or box). 

As soon as the driver said “lights,” “sign,” “object,” or read the words/numbers/symbol on a road 

sign, the experimenter pressed a key on the laptop computer, which placed a mark in the file to 

indicate detection. Associated with this mark would be the Global Positioning System (GPS)

coordinates of the vehicle along with the vehicle’s speed and the time the key was pressed.

Legend:

P = Pedestrian crossing assemblies.

D = Distractor signs.

O = Objects.

S = Stop signs.
84

After participants completed the driving portion of the study, researchers directed them to the 

discomfort glare portion of the study. The driver would position the vehicle at set distances from 

an assembly and would then answer questions following increases in the brightness of the 

beacons or LEDs. Figure 22 is a plan view of the discomfort glare study layout.

Figure 22. Diagram. Layout for the discomfort glare study.

COURSE DEVELOPMENT

Riverside Campus

The runway system on Texas A&M Riverside campus served as the test roadway for data 

collection. The runways offer a mixture of long straightaways, short intersecting segments, and 

curves. Researchers designed a route to permit participants to see every study assembly at a 

distance of at least 2,100 ft. To maximize the number of study assemblies, participants drove each 

runway section in both directions during a single lap. Each subject drove two laps, between which 

research team members changed the face of all distractor and study assembly signs, and the 

location of the objects (gray boxes, trash cans, and pedestrians).

Researchers added the following devices and objects to the Riverside runways: 

• Pedestrian crossing assemblies that included a sign with or without optional beacons. 

• Stop signs and distractor signs.

• Objects.
85

Pedestrian Crossing Assemblies

The study assemblies selected included the following:

• Two circular 12-inch beacons located above the sign (named C-A12 in the study) (see 

figure 23 and figure 24).

• Two circular 12-inch beacons located below the sign (C-B12) (see figure 25 and

figure 26).

• Two circular 8-inch beacons located below the sign (C-B8) (see figure 27 and

figure 28).

• One circular 12-inch beacon located above the sign and one circular 12-inch beacon 

located below the sign (C-V12) (see figure 29 and figure 30).

• Two rectangular beacons located above the sign (R-A) (see figure 31 and figure 32).

• Two rectangular beacons located below the sign (R-B), the format currently being 

used for the RRFB device (see figure 33 and figure 34).

• Sign with LEDs embedded into the border (LED) (see figure 35 and figure 36).

• Diamond-shaped sign with no beacons or LEDs (WO-B) (see figure 37 and

figure 38).

Figure 23. Photo. C-A12, lap A study assembly.
86

Figure 24. Photo. C-A12, lap B study assembly.

Figure 25. Photo. C-B12, lap A study assembly.

Figure 26. Photo. C-B12, lap B study assembly.
87

Figure 27. Photo. C-B8, lap A study assembly.

Figure 28. Photo. C-B8, lap B study assembly.

Figure 29. Photo. C-V12, lap A study assembly.
88

Figure 30. Photo. C-V12, lap B study assembly.

Figure 31. Photo. R-A, lap A study assembly.

Figure 32. Photo. R-A, lap B study assembly.
89

Figure 33. Photo. R-B, lap A study assembly.

Figure 34. Photo. R-B, lap B study assembly.

Figure 35. Photo. LED, lap A study assembly.
90

Figure 36. Photo. LED, lap B study assembly.

Figure 37. Photo. WO-B, lap A study assembly.

Figure 38. Photo. WO-B, lap B study assembly.

To serve as a baseline, one of the assemblies had neither beacons nor embedded LEDs. A mix of 

pedestrian crossing and other warning signs were selected for the study assemblies. The warning 

signs either had a pedestrian-related message or had a logo that was similar to a pedestrian. 

Study signs included Pedestrian Crossing (W11-2), Bike (W11-1), Playground (W15-1), 

Wheelchair (W11-9), TRAIL CROSSING (W11-15a), Pedestrian and Bike (W11-15), Deer 

(W11-3), T-intersection Ahead (W2-2), and Offset Intersections (W2-7L). All signs used in the 
91

assemblies had fluorescent yellow-green background so that the color of the signs used with 

beacons or LEDs would be similar. Table 93 describes the signs used along with the average 

retroreflectivity reading for each sign. 

Table 93. Study assemblies used in closed-course Riverside track study.

Portiona Sign faceb Beacc Lapd Positione Retrof

Driving

Trail Crossing C-A12 A P4(I), P5(II), P6(III), P8(IV) 783

Wheelchair C-A12 B P4(I), P5(II), P6(III), P8(IV) 750

Ped Crossing C-B12 A P2(I), P3(II), P4(III), P5(IV) 906

Bike C-B12 B P2(I), P3(II), P4(III), P5(IV) 783

Deer C-B8 A P8(I), P2(II), P3(III), P4(IV) 703

Ped Crossing C-B8 B P8(I), P2(II), P3(III), P4(IV) 904

Offset Intersection C-V12 A P6(I), P8(II), P2(III), P3(IV) 812

Ped Crossing C-V12 B P6(I), P8(II), P2(III), P3(IV) 907

Ped Crossing LED A P1 797

Offset Intersection LED B P1 798

Ped Crossing R-A A P3(I), P4(II), P5(III), P6(IV) 879

Playground R-A B P3(I), P4(II), P5(III), P6(IV) 779

Bike & Ped R-B A P5(I), P6(II), P8(III), P2(IV) 724

Ped Crossing R-B B P5(I), P6(II), P8(III), P2(IV) 884

Ped Crossing WO-B A P7 896

T-Intersection Ahead WO-B B P7 911

Comfort Change Differ C-B8 Comfort Discomfort Glare 502

Ped Crossing LED Comfort Discomfort Glare 691 a

Portion = part of the study, either driving (for those assemblies viewed during the driving portion) or comfort (for those 

assemblies viewed during the discomfort glare portion). All study signs used on the driving portion of the study (i.e., lap A 

and lap B) were 36-inch, fluorescent yellow-green background color on type IV sheeting. b

Sign Face = description of the face of the sign. Figure 23 to figure 38 show photographs of the sign faces and the types of beacons 

or LEDs included with the signs. c

Beac = type of beacons or LEDs included in the assembly: C = circular, R = rectangular, A = above, B = beneath, V = both above 

and beneath, 12 = 12-inch circular beacon, 8 = 8-inch circular beacon, LED = embedded LEDs on the edge of the sign, WOB = without beacons (i.e., no lights associated with assembly). d

Lap = assembly included in either lap A or B of the driving portion or as part of the discomfort glare potion. e

Position = location of the assembly on the course depending on the rotation. The first number (P1 to P8) indicates position while 

the value within the parentheses (I, II, III, IV) indicates the rotation. Four rotations of the signs occurred during the study (see

figure 21). f

Retro = average of four retroreflectivity readings in cd/lx/m2.

Assemblies Selected for Discomfort Glare Study 

For the discomfort glare study, researchers asked the participants to park the study vehicle 250 ft 

and 150 ft away from a traffic sign assembly (see figure 22). The two assemblies tested included 

two 8-inch circular beacons mounted below a sign that said “Change Differ” (see figure 39) and 

a pedestrian crossing sign with embedded LEDs (see figure 40). The Pedestrian Crossing sign 

was 36 inches, fluorescent yellow-green background color on type 4 sheeting. The “Change 

Differ” sign was 36 inches with an orange background. A plan view of the study area is shown in

figure 22, and an image from the start of the study area is shown in figure 41. 
92

Figure 39. Photo. Discomfort glare assembly with circular beacons.

Figure 40. Photo. Discomfort glare assembly with embedded LEDs.
93

Figure 41. Photo. View of discomfort glare course.

Flash Pattern for Assemblies

The flash pattern used with the assemblies was selected based on FHWA official interpretation 

4(09)-21 (I) released on June 13, 2012.

(26) The flash pattern used with the circular and 

rectangular beacons in this closed-course study is shown in table 94. Figure 42 shows an 

oscilloscope reading of the light output for the rapid flash pattern. For the LED-embedded sign, 

the research team decided to use the same flash pattern used with the right or top beacon in the 

other assemblies. Table 95 shows the flash pattern used with the LED-embedded sign with 

figure 43 showing the oscilloscope version.

Table 94. Rapid flash pattern.

Left or Bottom Beacon Right or Top Beacon Time Increment (milliseconds)

On Off 124

Off Off 76

On Off 124

Off Off 76

Off On 25

Off Off 25

Off On 25

Off Off 25

Off On 25

Off Off 25

Off On 25

Off Off 25

Off On 200

Total 800
94

Figure 42. Graph. Flash pattern for rapid-flashing beacons.

Table 95. LED-embedded sign flash pattern.

LEDs Time Increment (milliseconds)

Off 400

On 25

Off 25

On 25

Off 25

On 25

Off 25

On 25

Off 25

On 200

Total 800

Figure 43. Graph. LED-embedded sign flash pattern (uses same five-pulse pattern as that 

used by the right beacon in a rapid flash pattern).
95

Brightness of Beacons/LEDs

After the detection distance data and ramp-up data were collected, the light output for the 

beacons and LED-embedded signs were measured. To quantify the brightness of the pulsing 

lights, researchers used the TTI Photometric Range within the Visibility Laboratory. For each 

beacon and LED-embedded sign, a technician measured the 95th percentile peak intensity and 

the optical power of the device. For the discomfort glare devices, these measurements were taken 

at each of the six levels of the controller. The researcher took the measurements at a vertical 

angle of 0 degrees and a horizontal angle of 0 degrees. Figure 44 and figure 45 show examples of 

devices being measured.

Figure 44. Photo. LED-embedded sign mounted on goniometer.

Figure 45. Photo. Mounted 12-inch circular beacon.
96

Figure 46 shows example of the peak intensity and the 95th percentile intensity. Peak luminous 

intensity is the maximum luminous intensity for a given flash. For the flash in figure 46, the peak 

intensity is 3,715 cd, which is 6.2 times greater than the Society of Automotive Engineers (SAE)

standard J595 requirement for the center test point. The 95th percentile luminous intensity is the 

luminous intensity in which 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. The 95th percentile luminous intensity for the flash 

in figure 46 is 1,726 cd, which is 2.9 times greater than the SAE standard J595 requirement for 

peak luminous intensity at the center test point. 

Optical power is the integrated total of all flashes in a minute, in candela-seconds per minute

(cd-s/min). Figure 46 shows an example where the flash has an integrated value of 836 cd-s. 

Within a minute, there are 58.7 of these flashes; therefore, the optical power is 49,073 cd-s/min, 

which is 3.4 times the SAE standard J595 minimum optical power for the center test point. 

Figure 47 shows the measured optical power brightness for the devices used in the closed-course 

study. The measurements for those devices used in the driving portion have the same value 

(i.e., appear as a horizontal line) in the graph. The light intensity for these devices was not 

changed during the driving portion of the study. The two devices used in the discomfort glare 

portion of the study were changed from a setting of 1 to 6. The rectangular beacons had much 

higher optical powers compared with the other devices. Figure 48 shows the 95th percentile 

intensity brightness. Using 95th percentile intensity, the LED-embedded signs have similar 

values as the rectangular beacons. 

Figure 46. Graph. Example peak luminous intensity and optical power calculations. 
97

Figure 47. Graph. Optical power measurements for driving study assemblies compared 

with discomfort glare study controller settings (one setting in driving study).

Figure 48. Graph. 95th percentile intensity measurements for driving study assemblies 

compared with discomfort glare study controller settings (one setting in driving study).
98

Stop Signs and Distractor Signs Selected for Driving Study 

In addition to the study assemblies, distractor warning signs, distractor speed limit signs, and 

stop signs were included along the test route. The distractor signs served several purposes: to 

help hold participants’ attention on the course, to distract participants (to some extent) from the 

fact that the study assemblies were the focus of the research, and to provide some additional data 

points to observe detection and legibility distances. The stop signs were included on the course to 

ensure that the participants would stop at selected locations so that the researcher in the car could 

give instructions or make notes in the computer file.

The distractor signs included along the test route are listed in table 96, which also provides the 

average retroreflectivity reading for each sign. The retroreflectivity varies widely between the 

different stop and distractor signs; however, this was not a concern because these signs were not 

the focus of this study. 

Objects

Three objects were used in the study: a small gray wooden box, a trash can, and a pedestrian in 

blue medical scrubs. The pedestrian was selected because the assemblies being tested are for 

pedestrian crossings. The small gray wooden box has been used in other TTI nighttime detection 

studies.(15) Desired was a third object that would be between a pedestrian and small box in 

height; a trash can was selected. 

Objects could be located downstream of each study assembly and distractor sign. The research 

team decided that objects would not be located immediately downstream of the stop signs. 

Objects would also be located between signs so participants would need to look for objects at 

locations other than just when a sign was present. In most cases, if an object was present at a 

location in lap A, then an object was not present at that location in lap B. Table 97 lists the 

objects by position number for lap A and lap B. Figure 21 shows the positions for the objects.

The box was placed approximately 3 ft downstream of the sign (see figure 49). Because of the 

potential for the generator to block the view of the box when the box was used in conjunction 

with a study sign, the boxes were placed about 2 ft to the left of the sign post, which is 

approximately 11 ft from the edgeline of the lane. The dimension of the box was 9.25 inches at 

the widest point by 7.5 inches from the ground. Figure 50 provides a close-up of the box.
99

Table 96. Distractors and stop signs used in closed-course study.

Typea Sign faceb Lapc Positiond Retroe

S STOP A&B S1 116

S STOP A&B S2 13

S STOP A&B S3 23

S STOP A&B S4 22

S Blank A&B S5 79

S STOP A&B S6 6

S STOP A&B S7 49

S STOP A&B S8 17

S STOP A&B S9 152

D Speed Limit 42 A D1 398

D Speed Limit 35 B D1 297

D Speed Limit B3 A D2 268

D Speed Limit 45 B D2 289

D Enough Silent A D3 488

D Energy Supper B D3 272

D Speed Limit 35 A D4 865

D Speed Limit F4 B D4 42

D Stop Ahead (Symbol) A D5 616

D Simple Design B D5 513

D Loose Gravel A D6 99

D Rough Road B D6 107

D Speed Limit 48 A D7 268

D Speed Limit Y2 B D7 256

D Speed Limit H8 A D8 226

D Speed Limit 25 B D8 28

D Train (Symbol) A D9 61

D Always Animal B D9 483 a

Type = either D for distractor signs or S for stop signs. The distractor signs had various sizes, backgrounds, and sheeting 

because these signs were obtained from previous studies. The stop signs were either 30- or 36-inch typical stop signs,

except the sign at position S5. The sign at position S5 was alum high-intensity prismatic and did not have a legend (i.e., 

it was blank). The sheeting for the remaining stop signs varied because these signs were obtained from previous 

studies. b

Sign Face = description of the face of the sign.

c

Lap = assembly included in either lap A or B of the driving portion on as part of the discomfort glare portion. d

Position = location of the sign on the course (see figure 21). e

Retro = average of four retroreflectivity readings.
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Table 97. Object location on course.

Object positiona Followsb Lap A object Lap B object

O01 D1 small gray box empty

O02 P1 trash can empty

O03 D2 empty small gray box

O04 P2 empty pedestrian

O05 D3 pedestrian empty

O06 D4 empty trash can

O07 D5 trash can empty

O08 P3 small gray box empty

O09 D6 empty small gray box

O10 P4 trash can pedestrian

O11 After turn, S5 small gray box empty

O12 After turn, S5 empty trash can

O13 D7 empty empty

O14 P5 pedestrian small gray box

O15 After turn, S6 small gray box trash can

O16 P6 empty small gray box

O17 After turn, S7 small gray box empty

O18 P7 empty small gray box

O19 D8 trash can empty

O20 D9 empty pedestrian

O21 P8 pedestrian trash can a

Object position = location of the object on the course (see figure 21). b

Follows = the position of the sign preceding the object or that the object is after a turn (see figure 21).

Figure 49. Photo. Small gray box on course.
101

Figure 50. Photo. Close-up of small gray box.

The trash cans selected for this study were 42 inches high and 25 inches wide. The trash can was 

located 3 ft downstream of the sign (example shown in figure 51) when associated with a sign. 

Figure 52 shows an example of a pedestrian. Because of the length of the study and having three 

pedestrians present on each lap, several TTI staff members were used as pedestrians. The heights 

of the pedestrians ranged between 5 ft 2 inches and 6 ft 3 inches with the average height being 

5 ft 10 inches. The pedestrian stood approximately 3 ft downstream of the sign post and did not 

move as the participant approached the sign. Table 98 lists the study assemblies and distractor 

signs that preceded a pedestrian. Each of the study assemblies with circular or rectangular 

beacons was tested prior to a pedestrian at a minimum of one location. In some cases, the study 

assembly was tested prior to a pedestrian in two locations. 

Figure 51. Photo. Trash can on course.
102

Figure 52. Photo. Pedestrian on course.

Table 98. Study device and sign face by object position (view distance in ft) used with

pedestrians.

Study devicea Sign faceb

Object

O04 

(4308)

O10

(4900)

O14

(3965)

O21

(3617)

O05

(1452)

O20

(2137)

C-A12 Trail Crossing — — X X — —

C-A12 Wheelchair — X — — — —

C-B12 Bike X X — — — —

C-B12 Ped Crossing — — X — — —

C-B8 Deer — — — X — —

C-B8 Ped Crossing X X — — — —

C-V12 Ped Crossing X — — — — —

C-V12 Offset Intersection — — — X — —

R-A Ped Crossing — — X — — —

R-A Playground — X — — — —

R-B Bikes & Ped — — X X — —

R-B Ped Crossing X — — — — —

Distractor Always Animal — — — — — X

Distractor Enough Silent — — — — X — a

Study Device = type of device, either distractor sign or study assembly where the beacons or LEDs included in the study assembly 

have C = circular, R = rectangular, A = above, B = beneath, V=both above and beneath, 12 = 12-inch circular beacon, 8 =

8-inch circular beacon. b

Sign Face = description of the symbol or text on the face of the sign used with the study device.

X = object (pedestrian) was present beyond the study device with the given sign face.

— indicates object (pedestrian) was not present beyond the study device with the given sign face.
103

Site Selection for Study Assemblies, Distractor Signs, Stop Signs, and Objects

Warning signs and speed limit signs were placed throughout the course as distractor signs. These 

additional signs helped to maintain participants’ attention and interest between assemblies, 

distracted participants from the study’s focus, and provided additional information regarding 

legibility distance (because drivers could not as easily guess at the words/numbers appearing on 

the distractor signs). 

Figure 21 shows the route and the sign placements. Numbers on the graphic indicate the study 

assemblies (preceded by a “P”), distractor signs (preceded by a “D”), stop signs (preceded by an 

“S”), and objects (proceeded by an “O”), in the order in which they appeared on the route. 

Table 99 lists the travel distance and viewing distance for each sign or object. Viewing distance 

is the distance between where a participant turned onto the tangent and where the sign or object 

was located. It represents the theoretical maximum detection distance. Because of how some 

drivers positioned themselves at a turn or stop sign, longer distances could be measured. 

Position P7 on the course required the installation of the sign in the grass. The pre-existing lane 

markings are close to the edge of the concrete, which caused the sign to be located in the grass to 

maintain a 12-ft offset between the edgeline and the sign. The researchers selected the lightest 

study sign assembly for the in-ground installation at position P7. 

The Riverside runways are used by many groups within Texas A&M University system and were 

experiencing high demand during the summer 2012. While groups were willing to share the 

facility, some accommodations had to be made in this study. The signs in positions P1, S1, and 

S6 had to be removed at the conclusion of the daytime session (about 4 p.m.) and reinstalled 

prior to the start of the nighttime session (about 8 p.m.) to allow the landing of airplanes. 

Because a study assembly had to be removed every day, the researchers selected the assembly 

that would be easiest to remove, which was the sign with the embedded LEDs. 

A previous study revealed that the position of a device on the runway could affect the detection 

distance.(25) Because beacons can be seen several thousand feet in advance of the device, the 

researchers decided to have one set of beacons on each straightaway on the course. This limited 

the study to a maximum of eight study assemblies with two of those assemblies in positions 

where the limited advanced viewing distances may affect the results. Therefore, the two study 

assemblies with lowest priority for the study—the without-beacon sign and the LED-embedded 

sign—were placed at these locations (positions P1 and P7) (see figure 21). 

Another approach used to try to minimize the potential impact of position on the results was to 

rotate selected assemblies during the study. After data were recorded for a quarter of the 

participants, selected devices were rotated to a new position. With a goal of 64 total participants, 

researchers planned to rotate signs after data were collected for 16 (8 day and 8 night), 32, and 

48 participants. The rotation typically took more than 5 hours to uninstall, transport, and reinstall 

the devices at their new location. Another benefit of rotation was the ability to gather detection to 

pedestrian data for different assemblies while the background to the pedestrian remained 

constant. While selected assemblies were rotated, the objects were not rotated. An object for a 

given lap was present for all participants. Because of the number of participants for the study (set 

based on available budget) and the desire to have a reasonable number of participants viewing 
104

the assemblies in each rotation, the researchers decided to have only four rotations. Given the 

number of assemblies included in the study, the preference would have been to have more 

rotations; however, all studies have limitations and this is recognized as one of the limitations for 

this study. The position for each of the assemblies is shown in table 99.

Route Preparation

Several of the Riverside campus runways were already marked with yellow centerline striping and 

white edgeline striping to simulate rural roadways. Where striping was not present, the research 

team installed temporary raised pavement markers (RPM) to act as a roadway centerline. 

Signs without beacons were installed using a base as shown in figure 53. Figure 54 shows the 

larger base used with study assemblies that had the increased weight of beacons or embedded 

LEDs. In one case (position 7), the sign was installed in the ground because the edgeline was too 

close to the pavement edge to install the sign on the concrete.

Table 99. Order of device presentation on closed course.

Order Positiona

Travelb

(ft)

Viewc

(ft) Groupd Order Positiona

Travelb

(ft)

Viewc

(ft) Groupd

0 Start 0 Start Start 24 O10 3 4,900 Object

1 D1 2,000 1,100 Distract 25 S5 380 5,280 Stop

2 O01 3 1,103 Object 26 O11 630 630 Object

3 P1 1,291 2,394 Study 27 O12 949 800 Object

4 O02 3 2,397 Object 28 D7 717 1,517 Distract

5 S1 363 2,759 Stop 29 O13 3 1,520 Object

6 D2 2,239 2,239 Distract 30 P5 2,442 3,962 Study

7 O03 3 2,242 Object 31 O14 3 3,965 Object

8 P2 2,064 4,306 Study 32 S6 371 4,336 Stop

9 O04 3 4,308 Object 33 O15 667 667 Object

10 S2 371 4,679 Stop 34 P6 1,521 2,188 Study

11 D3 1,449 1,449 Distract 35 O16 3 2,191 Object

12 O05 3 1,452 Object 36 S7 978 3,169 Stop

13 D4 1,379 1,379 Distract 37 O17 1,035 1,035 Object

14 O06 3 1,382 Object 38 P7 1,457 2,492 Study

15 D5 1,240 1,240 Distract 39 O18 3 2,495 Object

16 O07 3 1,243 Object 40 D8 1,229 1,229 Distract

17 P3 1,380 2,623 Study 41 O19 3 1,232 Object

18 O08 3 2,626 Object 42 S8 896 900 Stop

19 S3 230 230 Stop 43 D9 2,134 2,134 Distract

20 S4 2,449 2,449 Stop 44 O20 3 2,137 Object

21 D6 2,972 2,972 Distract 45 P8 1,477 3,614 Study

22 O09 3 2,975 Object 46 O21 3 3,617 Object

23 P4 1,922 4,897 Study 47 S9 1,620 5,237 Stop a

Position = location of the object (or sign) on the course (see figure 21). b

Travel = distance traveled from previous position (ft).

c

View = distance (rounded) the item can be viewed after participant makes a turn (ft). d

Group = start, stop, distract, study, or object. Start and stop represents the starting point or ending point of the course, distract 

represents the distractor signs. “Study” represents the study assemblies (see table 93), and “Object” represents the objects (see

table 97).
105

Figure 53. Photo. Base for signs without beacons/LEDs.

Figure 54. Photo. Base for signs with beacons/LEDs.

The study assemblies were to be located a minimum of 360 ft prior to the stop sign. The 360 ft 

represents the stopping sight distance for 45 mi/h. It was selected as a compromise to have

enough distance before the end of the segment but also provide the largest amount of advanced 

viewing distance. The distance between the edgeline and the left edge of sign was 12 ft. The 

height between the pavement and the bottom of downward sloping arrow plaque was 6 ft 
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minimum based on MUTCD Section 2A.18 (see paragraph 06, page 42, option for secondary 

signs to be 1 ft less than 7 ft).

(2) The height between the pavement and the bottom of the 

diamondshaped sign was 7 ft minimum.

The distractor and stop signs also had a distance of 12 ft between the edgeline and the left edge 

of the sign. The distance between the pavement and the bottom of stop and distractor signs was 

6 ft or 7 ft minimum, respectively. 

For all signs except the sign with embedded LEDs, the sign changes between laps were 

accomplished by lifting the sign up and out of the bottom bracket and then out of the top bracket 

(see figure 55 and figure 56). For the embedded LED sign, the bolts holding the sign onto the 

pole were removed, and the sign was exchanged. The stop signs were not changed between the 

laps, only the distractor signs and the study signs.

Figure 55. Photo. Example of sign change.
107

Figure 56. Photo. Another example of sign change.

Practitioner Review

A practitioner review of the assemblies and the course occurred prior to collecting the participant 

study data. Several engineers local to the area were invited to review the course during both 

daytime and nighttime conditions. In addition to another TTI staff member, an engineer from a 

nearby city reviewed the assemblies in a late afternoon and then returned after dark to review the 

course during nighttime conditions. The review confirmed that the study assemblies did 

represent how a city would have integrated the beacons with the sign. The review did not result 

in any changes to the study assemblies or the course. Another city engineer reviewed the study 

course in the initial days of data collection, again confirming that the devices used were 

appropriate. 

DATA COLLECTION

Study Periods

The study was conducted under both daytime and nighttime conditions over 2 weeks in 

July/August 2012. The actual dates for the study were as follows:

• Wednesday, July 25, 2012 for practitioner review.

• Thursday, July 26–Friday, July 27, 2012.

• Monday, July 30–Friday, August 3, 2012.

• Sunday, August 5–Wednesday, August 8, 2012.
108

For July 26, 2012, sunset occurred at 8:24 p.m. On August 8, 2012, sunset occurred at 8:14 pm.

Initially, the arrival times for the participants were staggered with the idea that only one participant 

would be driving on the course at a time. That participant would drive the first lap, and members of 

the sign crew would follow behind the participant, changing signs in preparation for the second 

lap. This was the approach used successfully in previous studies. Because of the large number of 

sign and object changes required for this study, that approach resulted in large time gaps when the 

participant had to wait while sign changes and object changes were made and verified. 

After the initial days of data collection, the TTI team developed a different approach for 

conducting the study. The revised approach required additional vehicles for members of the field 

crew along with extra objects to facilitate the placing and removing of objects; however, the 

benefits were a much faster study time for the participants and fewer staff needed to conduct the 

study (only 8 instead of 10 staff members). Another bonus for this approach was that couples could 

be scheduled for similar times. Previous experience has shown that older couples who participate 

in a study at night prefer having this option.

In the revised approach, used for the majority of the data collection, two participants drove the 

course a few minutes apart. The researcher in the test vehicle gave instructions to the participants 

that resulted in the participants being one stop sign (or straightaway) apart. For example, when the 

first participant was leaving S3, the second participant would be leaving S2. This ensures that the 

headlights of the following participant would not shine into the first participant’s rear view mirror 

and that the two participants would not cross paths at any point on the course. Both participants 

would wait between laps while the field crew changed signs and objects. When first implemented, 

it took the field crew slightly less than 20 min to change all the signs and objects. They quickly 

improved their time to less than 10 min for sign and object changes between laps.

The study took about 1.5 hours from meeting the participant to the participant receiving his or her

payment (see table 100). Half of the participants drove during daylight hours and half during 

nighttime conditions. The following start times were used:

• 12:20 p.m.

• 2:20 p.m.

• 8:40 p.m.

• 10:10 p.m.

Table 100. Participant time in study.

Activity Time (min)

Escorting participant from front gate of Riverside to building where processing occurred 5

Initial processing, training 10

Drive to start, first lap 20

Wait time for sign changes (time allocated for this task decreased as sign crew became more 

familiar with duties) 10–20

Second lap, drive to discomfort glare location 20

Discomfort glare task 10

Final processing and payment 5

Total 80–90
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Participants

The initial intent was to recruit a group of participants composed of one-quarter males over 

55 years, one-quarter females over 55 years, one-quarter males under 55 years, and one-quarter 

females under 55 years. Within each of those demographic groups, the goal was to have an even 

distribution between those who drove during the day and those who drove at night and between 

those who drove lap A first and those who drove lap B first. Therefore, the following divisions 

were used in structuring participant recruitment:

• Light level: day or night.

• Age group: young (younger than 55 years) and old (55 years or older).

• Gender: male or female.

• Lap driven first: A or B.

These divisions resulted in 16 participant categories. The research goal was to have 4 participants 

in each of the 16 categories, resulting in 64 participants. The study included 71 participants

because participants were added to 1) replace participants whose data were not recorded 

successfully and 2) add additional data to offset missing data points not collected because signs 

were temporarily disabled or objects were not appropriately placed. The final participant pool is 

shown in table 101. Participants were at least 18 years old and possessed a valid driver’s license 

with no restrictions.

Table 101. Distribution of participants.

Age Gender

Day Night

Total

Lap A 

first

Lap B 

first

Lap A 

first

Lap B 

first

Younger than 55 Female 3 5 6 2 16

Male 5 5 3 6 19

55 or older Female 4 5 5 4 18

Male 6 2 3 7 18

Total 18 17 17 19 71

Participants were recruited by word of mouth, flyer distribution, and communication with people 

who participated in past studies and indicated an interest in future studies. Flyers with information 

about the study, location, contact information, dates, and compensation were distributed among 

friends and acquaintances and were posted in public places. Upon completion of the survey, 

participants received monetary compensation of $50. 

Tasks

The tasks for the participants while driving the closed course were to indicate when they could 

first perform the following:

• See warning lights.

• See road sign.

• Read the words or identify the symbol on the road sign.

• See object. 
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Instrumented Vehicle 

Two similar vehicles—2009 Ford Explorers—served as the participant cars for this experiment. 

The headlamps for these vehicles are 35 inches from the ground and 27 inches from center of the 

vehicle. Each vehicle was equipped with Qstarz BT-Q8181XT GPS receivers that reported the 

vehicle location at 10 Hz. The GPS receiver is a WAAS, EGNOS, and MSAS differential GPS 

device that uses WGS-84 datum with a position accuracy of 8.2 ft. Each receiver was affixed to 

the passenger side of the front windshield near the rear view mirror. TTI-developed software 

recorded the incoming GPS data stream into a text file along with single American Standard 

Code for Information Interchange (ASCII) character keyboard inputs from the researcher in the 

vehicle. The ASCII characters indicate responses by a participant during the study. 

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 would be collected. They were also given a Snellen visual acuity test and 

the Dvorine color vision test. 

To ensure consistency, the research team used scripts and slide shows to aid in providing 

instructions to each participant. The following script was used during intake:

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. The researcher will be using a 

distance measuring instrument to mark various points as you drive. 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 45 mi/h. 

Most of the route is marked with white and yellow striping just like you would 

see on an actual road. Part of the route is not striped, and when we reach these 

segments, the researcher will point you to the reflective pavement markings that 

will act as our road’s “center line.”

While driving the test course you will see a number of road signs. There may be 

multiple signs on each stretch of road. As you approach each of the road signs, 

please tell the researcher:

• If you can see warning lights.

• When you can see the road sign.
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• Then read the word or words on the sign as soon as you can read them. Some signs 

may not have words, and for these signs just tell me what you think the sign means. It 

is OK to make mistakes, just tell the researcher when you have made a mistake and 

the corrected word or words on the sign. 

• Occasionally, you may see something at the edge of the road, such as a small box, a 

trash can, or a pedestrian. If you do see one of these, please tell me by saying “box,” 

“can,” or “pedestrian.”

We will drive the entire course twice. There will be a brief pause at the end of 

your first drive through the course to allow field personnel time to set up the study 

area. 

While we want you to focus on the road signs as accurately as possible, your most 

important job is to drive safely, always paying attention to the road ahead and 

keeping the test vehicle under control. In addition, while this is a restricted area 

and there should not be any other vehicles in our area, on occasion there are 

vehicles on the roadway. So you will need to pay close attention to other vehicles 

on the roadway and obey all STOP signs you see.

Once in the study vehicle, a researcher will give you more specific instructions on 

the study procedures. If you do not have any questions, we will proceed to the 

study course. Do you have any questions?

A slide show on a laptop computer was used to illustrate the types of signs the participants might 

see. The slide show also included the three types of objects that would be placed on the course. 

Response Time Testing

As part of the intake process, a computer measured the participant and experimenter’s response 

times to develop a correction factor for each driver. Along the course, the experimenter pressed

a button when the participant indicated he or she saw lights, a road sign or an object, or read 

the words or identified the symbol on a road sign. There is a small lag between the participant 

speaking a word, e.g., “lights,” and the experimenter pressing the button. The lag could vary 

between the experimenters collecting the data. To address this concern, a pretest was 

developed to measure the lag time between when the participant saw a symbol on the computer 

screen and spoke the symbol’s name and when the experimenter pressed the button. The 

following four images were used in the exercise: down arrow, up arrow, plus sign, and black 

circle (or dot). Each symbol was repeated five times for a total of 20 random images. The task 

required the participant to identify which stimulus was present and say the correct word, a task 

analogous to the in-vehicle task of saying “lights” or “sign.” For the experimenters, the task 

was a simple reaction time test. They pressed a single button regardless of what the participant 

said, again analogous to the in-vehicle task.

The participant was instructed to say the name of the shape as quickly as possible once the image 

appeared on the computer screen. The experimenter pressed a button upon hearing the participant 

say the shape name. The software recorded the time difference between the shape appearing on 
112

the screen and when the button was pushed. The participant faced the computer screen, and the 

experimenter’s back was to the participant to avoid any anticipation on the part of the 

experimenter. Actual detection distance was determined from an average of the pretest reaction 

time for a participant along with the vehicle’s speed.

Vehicle Review

The participant was escorted to the instrumented vehicle and given a walk-through of the vehicle’s 

features. The participant was 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 instrumented vehicle on a closed course and 

were instructed to drive at a speed not exceeding 45 mi/h on the runways. They were asked to 

drive the runway system as though it were 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. Conversation between the 

participant and the experimenter was kept to a minimum to ensure that the participant did not 

miss identifying any of the signs and lights on the course.

First Lap of Driving Course

The participant drove the instrumented vehicle to the start position of the course, marked with an 

orange construction-zone barrel, waited while the researcher confirmed the lap (A or B) with the 

field crew and started recording a new data file on the computer, and then proceeded toward the 

first sign position. 

The participant was to signify detection of the item by saying a preselected word to indicate the 

presence of warning lights, road sign, or object, and to read the words or identify the symbol on a 

road sign as soon as the sign was close enough to be legible. The experimenter recorded the 

response on the computer. The following instructions were given to the participants:

Each time I tell you to start, you will do the following:

• Accelerate to a speed you are comfortable with, up to a maximum speed of 45 mi/h.

• When you first see warning lights or a road sign on the right side of the road, I would 

like for you to say “lights” or “sign.” If you initially saw lights, as soon as you can 

see the road sign, tell me “sign.” Third, as soon as you are sure what the sign says, 

please read me the words or numbers on the sign. If you change your mind and see 

that the sign says something different than you initially thought, read me the new 

word or words. If a sign has no words on it, tell me what you think the sign means.

• If you see an object such as a box or trash can close to the edge of the roadway, I 

would like for you say “box” or “trash can.” If you see a pedestrian close to the edge 

of the roadway, please say “pedestrian.”
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• The study course has several stopping points that we will use to provide you an 

opportunity to ask questions and rest, and provide me with an opportunity to give you 

further instruction if necessary. 

• You may stop at any time if you are uncomfortable.

• Because you will be watching for a lot of signs, we won’t be able to carry on other 

conversation while you are driving. 

• Please remember that safety comes first and follow all transportation and traffic laws.

If you don’t have any questions, we will start. Are you ready?

Second Lap of Driving Course

After completing the initial route, the participant was told to return to the orange construction 

barrel marking the route’s start point. Following notification from the sign crew that the signs 

had been changed from lap A to lap B (or vice versa), the researcher started a new data file and 

instructed the participant to proceed with the second lap. Following the second lap, the 

participant was directed to drive to the discomfort glare section of the course.

Discomfort Glare

At the beginning of the discomfort glare study, researchers asked the participants to park 250 ft 

away from sign 1 (the first two orange barrels) (see figure 22 or figure 41). After the participant 

parked the vehicle, researchers turned on the beacon and asked the participant to indicate 

whether the brightness of the light was comfortable, irritating, or unbearable, which were defined 

as follows:

• Comfortable—when the glare is not annoying and the signal is easy to look at.

• Irritating—when the glare is uncomfortable; however, the participant is still able to 

look at it without the urge to look away.

• Unbearable—when the glare is so intense that the participant wants to avoid looking 

at it.

After the participant rated the first level of the controller, a technician increased the controller 

setting to level two. This process continued until the participant indicated the brightness was 

unbearable or the technician reached level six on the controller (the highest setting for the 

device). Figure 48 shows the brightness measurements for each device at the six controller 

levels; measurements were taken at vertical and horizontal angle of zero.
114

Once the participant indicated the brightness was unbearable or the technician reached level six, 

the researcher asked the participant to move to the next position. In order, the positions were the 

following:

• LED-embedded sign, 250 ft.

• LED-embedded sign, 150 ft.

• 8-inch circular beacons, 250 ft.

• 8-inch circular beacons, 150 ft.

This order remained the same for every participant, which is a limitation of this study. 

DATA REDUCTION 

Participant Demographics

Table 102 lists the demographic information for the 71 participants. The large number that 

selected retired for employment (29 percent) is a reflection of the emphasis on having half of the 

drivers over 55 years of age.

Response Time 

The response lag time between participants and researcher were determined for each participant 

during the intake procedure. The average response time for each participant in conjunction with 

the experimenter who was collecting data for that participant was determined. A review of the 

data revealed several potential errors. Very long response times were deemed to be caused by 

some distraction to the participant or the experimenter, which happened occasionally in the 

intake room. Very short response times were eliminated because, on occasion, the experimenter 

accidentally pressed the button before the participant spoke. To eliminate these outliers, data 

points that were outside of two standard deviations of the average response time for that 

participant were removed. These steps removed 83 responses (about 6 percent). Table 103 lists 

the average response time by experimenter before and after removing data. In general, the 

response time was about 1 s for any experimenter.
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Table 102. Demographic information for 71 participants.

Characteristics Number (percent)

Gender Male 37 (52)

Female 34 (48)

Age Groups

< 55 35 (49)

≥ 55 36 (51)

Race

Caucasian 57 (80)

African American 1 (1)

Hispanic 8 (11)

Not reported 5 (7)

Employment

Full time 26 (37)

Part time 13 (18)

Retired 19 (27)

Not reported 5 (7)

Student 4 (6)

Other 4 (5)

Miles Driven Per Year

< 10,000 19 (27)

10,000–15,000 27 (38)

> 15,000 20 (28)

Not reported 5 (7)

Normal Driving Conditions

Rural roads 20 (28)

Freeways 3 (2)

City streets 31 (44)

Mixed 17 (26)

Table 103. Response time by experimenter.

Researcher

Number of 

responses

Average 

response 

time (ms)

Number of outliers 

(responses more than 

two standard 

deviations from 

average)

Number of 

responses 

without 

outliers

Average response 

time after outliers 

removed (ms)

L 499 972 28 471 931

C 438 1,072 26 412 1,014

O 320 947 20 300 906

J 100 1,049 6 94 1,018

S 40 1,071 3 37 1,038

All 1,397 1,006 83 1,314 960

A more detailed review of the response time data indicates that adjusting the detection distance 

for response time should occur uniquely for each participant rather than using a per-experimenter 

average response time. Figure 57 shows the plot of the responses measured for each participant 

before eliminating the outliers. Figure 58 shows the plot of the responses measured after 

eliminating the outliers. As can be seen in the plots, some participants had average response time 

below 0.8 s, while other participants’ response times averaged above 1.2 s. Therefore, the 

average response time by participant rather than by experimenter was used to adjust the detection 

distance. 
116

Figure 57. Graph. Measured response times by participant.

Figure 58. Graph. Response times by participant after removing outliers.

The measured detection distance was adjusted using the average response time for the participant 

and the speed of the vehicle at the point when the participant responded to a light or sign. The 

adjusted detection distance was an average of 2 percent higher than the GPS-based detection 

distance across all participants and sign/object locations. When the detection distances was 

shorter, such as to objects or reading the sign face, the average adjustment was about 

4 to 6 percent. The maximum response time adjusted distance was 47 ft, while the minimum was 

0 ft, a situation that occurred when the vehicle was stationary. 
117

Detection Distance 

The computer software program along with the GPS unit were used to determine the velocity and 

GPS coordinates when the driver identified a light, sign, or object, or when the participant read 

the sign. The data are recorded in a spreadsheet that contains a continuous stream of time, GPS 

coordinates, and velocity data. The time, velocity, GPS coordinates when the driver said “light,” 

“sign,” or “object,” or read the words on the sign were marked in the data streams. 

The GPS locations of each of the signs and objects were recorded before the study began. The 

detection distance was determined by subtracting the location of the sign or object from the 

locations marked by the experimenter in the vehicle. This calculated distance was then adjusted 

to account for the response time of the experimenter and participant. Average response time of 

the experimenter and specific participant was multiplied by the vehicle’s velocity at the time of 

identification to obtain the response distance. The response distance was added to the detection 

distance to obtain the adjusted detection distance. The adjusted detection distance was used in all 

the analyses. 

Box Plots

For some analyses, results are 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 59). 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 “whiskers” represent the data that lay 1.5 times 

beyond the interquartile range (IQR). This is the range between the 25th and 75th percentiles. 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 circles.

Figure 59. Diagram. Box plot details.

 

Outliers beyond 75th percentile

Plus 1.5*IQR 

Highest datum

Within 1.5*IQR

75th Percentile

25th Percentile

Lowest datum

Within 1.5*IQR

Outliers beyond 25th percentile

minus 1.5*IQR 

Median Score Interquartile Range (IQR)
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Data Cleaning 

Before proceeding with the statistical analyses, the data were reviewed in a search for records 

with duplicate information regarding the participant, and miscoded information regarding the 

response type, lap number, specific position of assembly or sign face presented in the assembly. 

These records typically generated duplicate data points that were corrected later. For example, a 

participant said “Word,” but after a few seconds said “Word” again for the same sign. After 

identifying all 262 instances (about 2 percent of the data stream), they were removed from 

further analysis.

As a second stage of data cleaning, extreme data points were identified from various box-plot 

representations of the data as potential outliers whenever their distance from the mean exceeded 

2.5 standard deviations (roughly corresponding to the 99.38th percentile of a normal 

distribution). Some of these early candidates were identified as anomalous and removed at that 

point. For example, for participants who were perceived as guessing at a long distance from the 

signs, the first stage of the data cleaning could not remove the instances when they guessed 

correctly, but in the second stage they appeared as extremely long distances. 

A final stage of this process consisted of observing outlying data points after performing the 

statistical analysis. In addition, some data points were identified in the second stage of data 

cleaning and therefore were removed at this stage. Eighteen data points were removed from the 

second and third stages of data cleaning.

RESULTS

Detection Distance—Light 

As expected, the detection distance to the beacons/LEDs was large. During the daytime, the 

average detection distance to the assemblies with beacons or LEDs was 2,244 ft. At night, the 

average detection distance was even longer, 3,236 ft. In several cases, the “light” detection 

distance was limited by the amount of viewing distance to the assembly. Therefore, the detection 

distances reported here could be shorter than the distance that a driver could detect a blinking 

beacon or LED. The conditions of the closed-course study; however, should result in very long 

detection distances. The participants were told to look for lights, and there were minimal 

background elements, such as billboards or street activity, competing for a driver’s attention. 

Detection Distance—Sign

The average detection distance to the sign (i.e., when the participants stated that they could see a 

sign) by type of sign (study assembly, distractor, or stop) along with age group and night or 

daytime conditions is shown in table 104. Caution is offered with the data because there are 

several elements that affect the results, such as viewing distance, position on the course, and the 

preceding sign or assembly. The focus of this study was legibility (i.e., the distance to read the 

study sign) and detection of the object located beyond a study sign. Therefore, the researchers 

did not attempt to control for those elements that could affect the detection of a sign and only 

presented averages and general observations regarding sign detection. The presence of 

beacons/LEDs along with the better quality retroreflectivity appears to contribute to the ability of 

the participants to see the sign from a greater distance when comparing the results for the 
119

assemblies (nighttime detection distance of 1,855 ft) with the results for the distractor signs 

(nighttime detection distance of 1,301 ft). Detection to the stop signs was lower than detection to 

other signs; however, there was always another sign located close to the stop sign, so the shorter 

detection distance may be affected by the presence of the other signs. Note that the average 

detection distances for all the sign types are greater than the stopping sight distance for 45 mi/h

(360 ft). The results in table 104 do reveal a difference owing to old versus young; however, a 

difference by daytime versus nighttime is not as evident. Overall, the participants had a slightly 

longer sign detection distance during the day compared with the night.

Table 104. Sign detection distance by sign type without attempts to control for external 

elements such as viewing distance that could affect results.

Old or 

Young Sign type

Daytime Nighttime

Count DD (ft) SD (ft) Count DD (ft) SD (ft)

Old Assembly 262 1,925 782 219 1,814 921

Old Distractor 314 1,121 420 297 1,235 523

Old Stop 288 737 460 280 834 540

Old Total 864 1,237 743 796 1,253 765

Young Assembly 262 2,311 829 284 1,886 733

Young Distractor 303 1,277 447 338 1,360 551

Young Stop 291 1,135 796 336 1,083 789

Young Total 856 1,545 871 958 1,419 768

Assembly 524 2,118 828 503 1,855 820

Distractor 617 1,198 440 635 1,301 541

Stop 579 937 680 616 970 697

Grand Total 1,720 1,390 823 1,754 1,344 771

Count = number of participants.

DD = average detection distance (ft).

SD = standard deviation detection distance (ft).

Legibility Distance

One of the objectives of this study was to determine whether sign legibility distance to a study 

assembly is affected by the shape, size, or placement of the beacon/LEDs. The preliminary 

evaluations revealed confounding issues between the sign face and the beacon type, which is not 

surprising because each beacon type had only two sign faces. To handle this issue in the analysis, 

the following two approaches were used: 

1. Pedestrian Crossing Sign Only Data: This approach focused on the findings when the 

Pedestrian Crossing sign was used in an assembly.

2. All Assemblies Data: This approach included a new variable to group the sign faces 

into one of three groups so that the data for all assemblies could be considered. The 

following groups were identified for the new variable “sign.fam” (for sign family):

o Ped X-ing sign: Pedestrian Crossing sign (W11-2).

o Other common signs: Deer (W11-3), T-Intersection Ahead (W2-2), and Offset 

Intersections (W2-7L).
120

o Uncommon signs: Bike (W11-1), Playground (W15-1), Wheelchair (W11-9), 

Trail Crossing (W11-15a), and Ped and Bike (W11-15).

To show the range of legibility distance being considered, table 105 lists the average and 

standard deviation of the legibility distance for daytime and nighttime. The analyses were 

separated into daytime and nighttime because 1) daytime and nighttime detection distances

differed considerably and 2) to permit the opportunity to determine whether variables were

affecting legibility distances differently in day and night conditions. 

Table 105. Legibility distance by assembly type and sign face.

Assembly Sign face

Daytime Nighttime

Count DD (ft) SD (ft) Count DD (ft) SD (ft)

A: C-A12 Trail Crossing 34 284 100 34 226 89

A: C-A12 Wheelchair 35 714 350 35 538 206

A: C-B12 Bike 34 617 199 36 602 467

A: C-B12 Ped X-ing 35 993 410 36 786 236

A: C-B8 Deer 34 1,007 397 36 643 279

A: C-B8 Ped X-ing 35 1,002 330 35 724 263

A: C-V12 Offset Intersection 34 973 381 35 777 282

A: C-V12 Ped X-ing 36 1,028 320 36 780 284

A: LEDs Offset Intersection 35 970 423 35 822 352

A: LEDs Ped X-ing 34 1,020 290 35 839 332

A: R-A Ped X-ing 33 994 360 35 708 190

A: R-A Playground 35 536 294 36 377 204

A: R-B Bike & Ped 34 535 276 33 423 259

A: R-B Ped X-ing 34 980 377 36 672 221

A: WO-B Ped X-ing 34 1,061 437 35 784 233

A: WO-B T-Intersection 35 1,102 414 36 886 326

Grand Total 551 864 415 564 663 326

Count = number of participants.

DD = average detection distance (ft).

SD = standard deviation detection distance (ft).

Each dataset was analyzed using linear mixed effects models (LMM). These kinds of models 

combine characteristics from both linear regression and analysis of variance (ANOVA) with 

replication. The model was specified such that the data structure, known associations between 

variables, and systematic variation in the response variable were appropriately accounted. The 

analysis treated the co-dependency of data points from different drivers by explicitly adding a 

random effect for each participant in the experiment. The order of the laps was also treated this 

way. Within the blocking structure described above (i.e., light condition/driver/lap number), the 

analysis incorporated fixed effects for other variables of interest. In the case of legibility 

distance, the fixed effect variables were age group, sign family, and type of assembly. Estimates, 

confidence intervals, and conclusions were later extracted for these effects. 

Finally, the analysis applied weights in inverse proportion to the estimated travel time between 

the point of detection and the assembly. This adjustment was necessary because data showed 

larger variability at greater distances from the assemblies. (This condition is known as 

heteroskedasticity in the statistics literature.) Preliminary examinations considered several other 

variables, such as position on the course and view distance. For some of the analyses, interactive 
121

terms were included to account for interaction between variables. All statistical analyses were 

performed using open source data analysis packages. (See references 27 through 30.)

It was necessary to adjust the model standard errors when comparing various pairs of assemblies. 

In such instances, the confidence in the model estimates decreases as the number of comparisons 

increases. This occurs because the chance of a type-I error is greater for simultaneous 

comparisons than for single comparisons. 

A similar adjustment was made when comparing groups of assemblies. The reason given above 

is valid in this case too, but more important, the adjustment also permits accounting for 

correlation among the model estimates, which greatly affects the standard error of group 

comparisons. An example of group comparisons is investigating for a difference between all 

assemblies with rectangular and all assemblies with circular beacons. 

Daytime Legibility Distance

An objective of this study was to investigate whether the shape of the beacons (circular or 

rectangular), the size of the beacon (e.g., 12 inches or 8 inches), or the placement of the beacon 

(e.g., above or below the sign) affected the results.

Table 106 shows the LMM results, and table 107 shows the ANOVA results for daytime. 

Preliminarily, the estimates for the effects of particular assemblies do not seem statistically 

significant (compared with their standard errors). This observation is consistent with the 

ANOVA results for the model (showed at the end of the table) where Assembly as a group is a 

statistically insignificant factor to explain legibility distance variation (p-value of 0.0565).

Table 108 shows the daytime pair comparison results from the model in table 106. The shape of 

the beacon—circular or rectangular—did not have an effect on legibility distance as shown by 

the non-significant result for the paired comparison of circular to rectangular. The individual pair 

comparisons of C-B12 to R-B and R-A to C-A12 were also not statistically significant, 

indicating that the daytime legibility distances are similar for each pair. The comparison of the 

legibility distance with comparison of size of beacon (i.e., C-B8 and C-B12) also did not show a 

significant difference between legibility distances. 

The evaluation that examined legibility when the beacons were placed above or below the signs 

found no significant difference in distances. When reviewing the comparisons of individual 

pairs, the legibility distance for R-A and R-B were similar; however, C-B12 and C-A12 were 

different (statistically significant) (see table 108). This finding is additional evidence that the 

uncommon signs are confounding the results. When the circular beacons were located below the 

sign, drivers could read the signs at a greater distance upstream than when beacons were located 

above the sign. The assemblies with the 12-inch circular beacons above the sign had uncommon 

signs (Trail Crossing and Wheelchair) while the assemblies with the circular beacons below the 

sign had the more familiar signs of Ped X-ing and Bike, which confounds the validity of this 

finding. To offset this limitation, only the data when the Pedestrian Crossing sign was present 

was examined. 
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Table 106. Linear mixed-effects model results using all assemblies for daytime legibility 

distance.a

Variable Value Std. error DF t-value p-value

Reference Levelb 560.6991 55.77835 490 10.05227 0.0000

O.Y: young 531.4206 63.2773 33 8.398282 0.0000

Sign.Fam: Ped X-ing 92.5412 36.11634 490 2.562309 0.0107

Sign.Fam: Uncommon -216.2926 46.45764 490 -4.655694 0.0000

Type_AsA: C-A12 -54.663 30.64454 490 -1.783778 0.0751

Type_AsA: C-B12 68.5406 35.13588 490 1.950728 0.0517

Type_AsA: C-B8 73.4926 46.41126 490 1.583508 0.1140

Type_AsA: C-V12 72.4622 46.20551 490 1.568259 0.1175

Type_AsA: LEDs 56.3263 47.01232 490 1.198118 0.2315

Type_AsA: R-A 6.2942 33.8891 490 0.185729 0.8527

Type_AsA: WO-B 89.4155 47.13876 490 1.896858 0.0584

O.Y: young X Sign.Fam: Ped X-ing -37.1006 57.0205 490 -0.650654 0.5156

O.Y: young X Sign.Fam: Uncommon -279.2907 52.69738 490 -5.299899 0.0000 a

Notes:

1. Linear mixed-effects model fit by REML.

2. Fixed effects: Adj_Dis ~ O.Y + Sign.Fam + Type_As + O.Y X Sign.Fam. b

Reference level in the model has the following conditions:

• O.Y. (age: old or young) = older driver.

• Sign.Fam (sign family) = common signs.

• Type_AsA (assembly type, see figure 23 to figure 38) = R-B.

Std. error = Standard error of value.

DF = Degrees of Freedom.

Table 107. ANOVA results using all assemblies for daytime legibility distance.

Variable numDF denDF F-value p-value

(Intercept) 1 490 789.9444 < .0001

O.Y 1 33 60.4778 < .0001

Sign.Fam 2 490 284.1375 < .0001

Type_As 7 490 1.9768 0.0565

O.Y X Sign.Fam 2 490 21.9561 < .0001

Table 108. Daytime legibility distance multiple comparison using all assemblies data.

Test Item Comparison Estimate Std. error Z-value Pr(>|z|) a

Simultaneous 

Tests for 

General Linear 

Hypotheses

Place (Above) - ( Below) = 0 -58.455 24.211 -2.414 0.08335 .

Shape (Circular) - (Rectangular) = 0 3.792 23.555 0.161 0.99978

Size (C-B8 ) - (C-B12) = 0 4.952 46.579 0.106 0.99996

Confirmation 

(Individual 

Pair 

Comparisons)

Shape (C-B12 ) - (R-B) = 0 68.541 35.136 1.951 0.23267

(R-A) - (C-A12) = 0 60.957 33.396 1.825 0.29364

Place (R-A) - (R-B) = 0 6.294 33.889 0.186 0.9996

(C-B12 ) - (C-A12) = 0 123.204 35.553 3.465 0.00334** a

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001.

When the evaluation only considered those conditions when the pedestrian crossing sign was 

present, no significant differences between assemblies (LEDs, WO-B, R-B, R-A, C-V12, C-B8, 

C-B12) were identified. Stated in another manner, the presence of a yellow rapid-flashing beacon 

did not affect the legibility distance to the pedestrian crossing sign during daytime conditions.
123

Nighttime Legibility Distance

The linear mixed-effects model for the nighttime legibility distance when using the data for all 

assemblies (see table 109) shows that the following variables are significant: age (old, young), 

sign family, assembly type, and interaction between age and sign family. Assembly type was not 

significant at the 0.05 level in the daytime model while it was in the nighttime model. This 

suggests assembly type influences sign legibility at night more than it does during daytime 

conditions. Table 110 shows the ANOVA results.

Table 111 shows the results for the nighttime comparisons regarding the shape, size, and 

placement of beacon. As shown in table 111, the circular beacons were associated with longer 

legibility distances as compared to rectangular beacons. Examining the pairs of circular to 

rectangular beacons in similar positions revealed that the shape of the beacons was not 

significant when the beacons were above the sign (see R-A compared with C-A12); however, the 

shape of the beacons was significant when the beacons were below the sign (see C-B12 with

R-B). The participants read the sign 157 ft earlier when the beacon was a 12-inch circular beacon 

as compared to a rectangular beacon located below the sign.

The comparison of the legibility distance for size of beacon (i.e., C-B8 and C-B12) also shows a 

significant difference between legibility distances. Drivers were able to read the sign earlier with 

12-inch lenses compared with 8-inch lenses. 

The placement of the beacon above or below the sign was also significant; however, examining 

the specific pair comparisons reveals no significant difference between R-A and R-B and a 

significant difference between CA-12 and CB-12. Therefore, this comparison may be 

compromised by the sign choice for C-A12.

Table 109. Linear mixed-effects model results using all assemblies for nighttime legibility 

distance.

a

Variable Value Std. error DF t-value p-value

Reference Levelb 479.2589 51.06080 501 9.386044 0.0000

O.Y: young 294.0365 59.69037 34 4.926029 0.0000

Sign.Fam: Ped X-ing 60.1758 29.94397 501 2.009615 0.0450

Sign.Fam: Uncommon -243.4851 36.84204 501 -6.608892 0.0000

Type_AsA: C-A12 4.2572 24.39833 501 0.174495 0.8615

Type_AsA: C-B12 156.5475 27.49044 501 5.694617 0.0000

Type_AsA: C-B8 -18.2307 34.48298 501 -0.528688 0.5973

Type_AsA: C-V12 59.7811 35.08480 501 1.703902 0.0890

Type_AsA: LEDs 95.5236 36.33491 501 2.628975 0.0088

Type_AsA: R-A 16.0609 24.88700 501 0.645354 0.5190

Type_AsA: WO-B 145.0945 36.24990 501 4.002619 0.0001

O.Y: young X Sign.Fam: Ped X-ing -99.4175 41.93428 501 -2.370793 0.0181

O.Y: young X Sign.Fam: Uncommon -144.0525 38.82845 501 -3.709974 0.0002 a

Notes:

1. Linear mixed-effects model fit by REML.

2. Fixed effects: Adj_Dis ~ O.Y + Sign.Fam + Type_As + O.Y:Sign.Fam. b

Reference level in the model has the following conditions:

• O.Y. (age: old or young) = older driver.

• Sign.Fam (sign family) = common signs.

• Type_AsA (assembly type, see figure 23 to figure 38) = R-B.
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Table 110. ANOVA results using all assemblies for nighttime legibility distance.

Variable numDF denDF F-value p-value

(Intercept) 1 501 405.479 < 0.0001

O.Y 1 34 15.1728 0.0004

Sign.Fam 2 501 313.0319 < 0.0001

Type_As 7 501 9.6389 < 0.0001

O.Y X Sign.Fam 2 501 6.9101 0.0011

Table 111. Nighttime legibility distance multiple comparisons using simultaneous tests for 

general linear hypostheses.

Groupa Item Linear Hypotheses Estimate Std. error z-value Pr(>|z|) b

ALL Place (Above) - ( Below) = 0 -68.11 18.65 -3.651 0.0017**

ALL Shape (Circular) - (Rectangular) = 0 72.37 18.08 4.002 < 0.001***

ALL Size (C-B8) - (C-B12) = 0 -174.78 35.70 -4.896 < 0.001***

IPC Shape (C-B12) - (R-B ) = 0 156.55 27.49 5.695 < 0.001***

IPC Shape (R-A) - (C-A12) = 0 11.80 24.33 0.485 0.9835

IPC Place (R-A) - (R-B) = 0 16.06 24.89 0.645 0.9538

IPC Place (C-B12) - (C-A12) = 0 152.29 27.76 5.487 < 0.001***

Sign Assembly (R-A) - (WO-B ) = 0 -74.58 37.97 -1.964 0.12317

Sign Assembly (R-B) - (WO-B ) = 0 -123.70 37.64 -3.287 0.00293**

Sign Assembly (C-B8) - (WO-B ) = 0 -84.96 37.92 -2.240 0.06509 a

Group:

1. ALL = all assemblies data.

2. IPC = All Assemblies Data: Confirmation (Individual Pair Comparisons).

3. Sign = Pedestrian Crossing Only Data (i.e., Data for Assemblies when Pedestrian Crossing Sign is Present). b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001.

Box plots were generated to aid in the review of the Pedestrian Crossing sign data. Figure 60

shows the legibility distance for young drivers to assemblies with a pedestrian crossing symbol, 

while figure 61 is a similar plot for old drivers. The average legibility distance was similar 

between the R-B and the C-B8 or the R-A for both young and old drivers. In the remaining cases, 

the average legibility distance for R-B was less than the legibility distance for C-B12, C-V12, 

LEDs, and WO-B. 

Figure 60. Graph. Box plots for nighttime legibility distance for assemblies with pedestrian 

crossing sign for young participants.
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Figure 61. Graph. Box plots for nighttime legibility distance for assemblies with pedestrian 

crossing sign for old participants.

The evaluation that considered only those conditions when the Pedestrian Crossing sign was 

present again confirms the finding that the yellow rapid-flashing beacon is affecting drivers’

ability to read signs. Table 112 contains the results of the statistical model and shows that the 

following variables were significant: age (young or old), R-A, R-B, and C-B8. Next, the pair 

comparisons were done for these beacons (see table 111). The pair comparison showed that the 

difference between R-A and WO-B was not significant, that the difference between C-B8 and 

WO-B was significant at a p-value less than 0.1, and that the difference between R-B and WO-B 

was significant at a p-value less than 0.05. Stated in another manner, the presence of the RRFB

located below the sign affected the legibility distance to the Pedestrian Crossing sign during 

nighttime conditions. Drivers were 124 ft closer to the sign before they could read the symbol 

compared with the distance when they could read the symbol when an RRFB was not present.

Table 112. Linear mixed-effects model results for assemblies with pedestrian crossing sign 

for nighttime legibility distance.

a

Variable Value Std. error DF t-value p-value

Reference Levelb 664.5759 49.31322 206 13.476627 0.0000

O.Y: young 208.1935 58.40199 34 3.564836 0.0011

Type_AsA: R-A -72.9047 38.09806 206 -1.913607 0.0571

Type_AsA: R-B -112.0146 37.26261 206 -3.006086 0.0030

Type_AsA: C-B12 2.7815 38.06915 206 0.073063 0.9418

Type_AsA: C-B8 -80.8886 37.88780 206 -2.13495 0.0339

Type_AsA: C-V12 -29.944 37.89882 206 -0.790103 0.4304

Type_AsA: LEDs 20.2158 38.81704 206 0.520798 0.6031 a

Notes:

1. Linear mixed-effects model fit by REML.

2. Fixed effects: Adj_Dis ~ O.Y + Type_As. b

Reference level in the model has the following conditions:

• O.Y. (age: old or young) = older driver.

• Type_AsA (assembly type, see figure 23 to figure 38) = WO-B.
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Key Findings Regarding Legibility Distance

For the analysis that focused on the legibility distance, which is the distance between the sign 

and the participant when the participant reads the message on the sign, results indicate the 

following:

• As expected, the legibility distance for signs during the day is greater than the 

legibility distance for signs at night. 

• Younger driver legibility distance is greater than older driver legibility distance. 

Previous studies on crosswalk markings and on stop signs with LEDs found age to be 

not significant.(25,31) In those studies, the participant did not have to “read” a sign. In 

the crosswalk marking study, the participant indicated when they could see the 

markings. For stop signs, the stop sign’s shape and color are more important than 

reading the word “stop.” Finding age to be significant indicates that future studies of 

beacons may need to focus on older participants explicitly. 

• The symbol or word(s) on the sign face confounds the results. When the study was 

planned, different signs were desired to provide variety during the study. The 

researchers were concerned that the driver would always anticipate a pedestrian 

crossing sign when he or she saw a beacon, so alternative signs were needed. The 

researchers selected signs that could be associated with a pedestrian crossing, such as 

pedestrian crossing, trail crossing, bike, playground, bike & ped, and wheelchair. The 

researchers also selected signs that had a look similar to the pedestrian crossing sign 

such as the deer crossing, t-intersection, and offset intersection signs. Because the 

type of sign confounds the results, understanding how the beacons/LEDs influence

the detection distance is more complex. A new variable, sign family, was added to the 

evaluation to reflect whether the symbol is common or uncommon. In the sign family 

variable, the signs were grouped into the following three classes: pedestrian crossing, 

other common signs (deer, t-intersection, offset), and uncommon signs (wheelchair, 

trail crossing, bike, playground, and bike & ped). Finding that the sign face is 

significant in this study indicates that the profession needs to be concerned with the 

messages placed on a sign when the sign is used with beacons/LEDs. The estimated 

closed-course legibility distance to an uncommon sign (e.g., trail crossing) for an 

older driver at night was 236 ft, which while greater than the MUTCD estimation of 

legibility distance for a sign with 6-inch letters, is much less than the legibility 

distance for the symbol signs. 

• During the nighttime, drivers were able to read the sign earlier with 12-inch lenses 

compared with 8-inch lenses. During daytime, the distance to read the sign was 

similar for 12- and 8-inch lenses.

• The type of assembly was significant at night and nearly not significant 

(p-value = 0.0565) during the day. This indicates that the effects of the beacons/LEDs 

on reading the message on the sign are more influential during nighttime conditions, 

an expected finding.
127

• Based on the sign legibility results, the suggested alternative beacon arrangement for 

testing in the on-road portion is the C-B12 assembly; the primary beacon assembly is 

the R-B assembly. 

Observations Regarding Legibility Distance

• The presence of a yellow flashing beacon communicates warning to a driver. Even if 

the flashing beacon limits the ability to read a sign, its presence can warn drivers to 

take additional care at the location. Unfortunately, the extensive and continuous use 

of the flashing yellow beacon on U.S. highways may not effectively communicate to 

drivers the needed action of slowing down or searching for a potential roadway entry. 

The use of a specific flash pattern, however, could offset some of these concerns. The 

profession should investigate the message that different styles of blinking lights (e.g., 

rapid flash versus uniform flash, etc.) communicate to drivers.

Detection Distance—Object 

An objective of this study was to determine whether the detection of objects located beyond an 

assembly was affected by the shape, size, and/or placement of the yellow RRFBs/LEDs. This 

analysis is separated into daytime and nighttime because of large differences in detection 

distance between day and night and to determine whether variables were affecting object 

detection distances differently in day and night conditions. Each set was analyzed using LMMs. 

A number of different evaluations were conducted to focus on different elements of the study, 

such as day/night, different groups to account for sign face, or upstream conditions. The

following variables were considered:

• Age (young or old).

• Object type (box, trash can, or pedestrian).

When focusing on characteristics of the study assemblies, the following variables were 

considered:

• Previous assembly (C-A12, C-B12, C-B8, C-V12, R-B, R-A, or LED). Preliminary 

results demonstrated that the WO-B device had too many unique qualities and should 

not be included in the evaluation. Those qualities included having only one object—

the box—that followed the sign. 

• Previous sign family (Ped X-ing, common signs, and uncommon signs).

When evaluating the effects of all upstream conditions, the following three new groups were 

created: 

• Signs w/Bea-LED (C-A12, C-B12, C-B8, C-V12, R-B, R-A, or LED).

• Signs without Bea-LED (included the distractor signs and the WO-B assembly).
128

• Turn (represents the situation when the object was not located beyond a sign, i.e., the 

object was located alongside the road after the driver had just made a turn on the 

course).

To provide an appreciation of the range of object detection distance considered, table 113 lists 

the average and standard deviation of the object detection distance for the pedestrian, Table 114

is similar data for the trash can, and table 115 contains data for the small box.

Figure 62 shows the box plots for the data for the daytime while figure 63 shows the nighttime 

box plots. The limits of the box plots are at 25th and 75th percentiles. The whiskers were drawn 

at 1.5 times the interquartile range, and the widths were drawn proportional to the square root of 

the number of observations.

Table 113. Object detection distance for the pedestrian by upstream device and sign face. 

Upstream devicea

Day Night

Countb DD (ft) SD (ft) Count DD (ft) SD (ft)

A: C-A12-Trail Crossing 16 962 617 11 54 33

A: C-A12-Wheelchair 11 668 414 5 181 180

A: C-B12-Bike 17 710 396 15 195 89

A: C-B12-Ped X-ing 8 1,361 502 9 164 89

A: C-B8-Deer 11 1,020 472 9 76 44

A: C-B8-Ped X-ing 16 790 349 10 149 69

A: C-V12-Offset Intersection 8 1,087 986 6 84 51

A: C-V12-Ped X-ing 8 554 185 9 154 110

A: R-A-Ped X-ing 8 1,242 527 11 177 119

A: R-A-Playground 8 1,066 400 3 99 39

A: R-B-Bike & Ped 19 980 788 18 82 75

A: R-B-Ped X-ing 8 479 204 5 75 12

D: Always Animal 34 1,228 438 30 98 105

D: Energy Supper 0 0 NAc 1 102 NAc

D: Enough Silent 35 664 312 24 89 55

Total Count/Average Distance 207 911 539 166 116 93 a

A: = assembly (see figure 23 to figure 38), D: = distractor sign (see table 96). b

Count = number of participants, DD = average detection distance (ft), SD = standard deviation detection distance (ft). c

NA = not applicable, two or more points are required for a standard deviation.
129

Table 114. Object detection distance for the trash can by upstream device and sign face.

Upstream devicea

Day Night

Countb DD (ft) SD (ft) Count DD (ft) SD (ft)

A: C-A12-Trail Crossing 9 722 215 6 135 67

A: C-A12-Wheelchair 7 992 404 7 53 18

A: C-B12-Ped X-ing 8 684 387 6 102 21

A: C-B8-Deer 8 823 249 5 79 55

A: C-B8-Ped X-ing 10 1,302 964 9 149 209

A: C-V12-Ped X-ing 8 1,031 427 5 140 114

A: LEDs-Ped X-ing 27 617 242 21 147 115

A: R-A-Ped X-ing 7 1,341 978 5 155 92

A: R-B-Ped X-ing 8 591 453 11 117 192

D: Speed Limit F4 31 657 336 22 75 45

D: Speed Limit H8 34 949 154 30 187 91

D: Stop Ahead (symbol) 32 763 185 29 174 81

Turn 69 650 109 65 276 137

Total Count/Average Distance 258 773 378 221 179 133 a

A: = assembly (see figure 23 to figure 38), D: = distractor sign (see table 96). b

Count = number of participants, DD = average detection distance (ft), SD = standard deviation detection distance (ft).

Table 115. Object detection distance for the box by upstream device and sign face.

Upstream devicea

Day Night

Countb DD (ft) SD (ft) Count DD (ft) SD (ft)

A: C-A12-Trail Crossing 0 0 NAc 1 100 NAc

A: C-A12-Wheelchair 12 359 153 15 153 84

A: C-B12-Bike 5 228 52 6 110 22

A: C-B12-Ped X-ing 6 685 380 6 160 112

A: C-B8-Deer 6 518 248 12 188 234

A: C-B8-Ped X-ing 0 0 NAc 1 137 NAc

A: C-V12-Offset Intersection 11 459 400 7 112 112

A: C-V12-Ped X-ing 8 233 176 7 190 102

A: R-A-Ped X-ing 9 233 216 8 337 293

A: R-A-See Saw 9 294 143 13 117 100

A: R-B-Ped X-ing 14 311 231 13 132 64

A: WO-B-T-Intersection 27 200 170 33 145 62

D: Rough Road 24 237 174 27 138 85

D: Speed Limit 42 12 309 141 23 150 92

D: Speed Limit 45 26 293 164 27 209 94

Turn 56 235 139 81 166 84

Total Count/Average Distance 225 286 210 280 162 110 a

A: = assembly (see figure 23 to figure 38), D: = distractor sign (see table 96). b

Count = number of participants, DD = average detection distance (ft), SD = standard deviation detection distance (ft).

c

NA = not applicable, two or more points are required for a standard deviation.
130

Figure 62. Graph. Box plot of daytime object detection distance by upstream condition.

Figure 63. Graph. Box plot of nighttime object detection distance by upstream condition.
131

Daytime Object Detection Distance

Table 116 shows the LMM, and table 117 shows the ANOVA for daytime object detection 

distance with upstream conditions (i.e., this evaluation includes objects located behind distractor 

signs and after turns). All variables considered were significant: type of object (box, trash can, or 

pedestrian), age group (young or old), and upstream conditions. Finding upstream conditions 

significant indicates that the presence of a sign assembly and the characteristics of that sign 

assembly affect a participant’s ability to detect an object.

Table 116. Model for daytime object detection distance considering upstream condition.

a

Variable Value Std. error DF t-value p-value

Reference Levelb 485.3553 35.77571 418 13.566617 0.0000

Object: box -287.9107 33.86529 418 -8.501646 0.0000

Object: can -68.0271 39.98690 418 -1.701234 0.0896

O.Y: young 317.8674 57.58077 33 5.520374 0.0000

Upstream2: Distractor -45.9384 22.90688 418 -2.005440 0.0456

Upstream2: Turn -119.9760 34.51648 418 -3.475905 0.0006

Object: box X O.Y: young -254.9557 54.31000 418 -4.694452 0.0000

Object: can X O.Y: young -35.7910 72.27445 418 -0.495210 0.6207 a

Notes:

1. Linear mixed-effects model fit by REML.

2. Fixed effects: Adj_Dis ~ Object * O.Y + Upstream2. b

Reference level in the model has the following conditions:

• Object = pedestrian.

• O.Y. (age: old or young) = older driver.

• Upstream2 = assembly.

Table 117. ANOVA results using upstream conditions daytime object detection distance.

Variable numDF denDF F-value p-value

(Intercept) 1 418 276.53328 < .0001

Object 2 418 149.20799 < .0001

O.Y 1 33 16.42528 0.0003

Upstream 2 2 418 5.64380 0.0038

Object X O.Y. 2 418 13.87962 < .0001

Table 118 shows the results when focusing on objects near study assemblies (i.e., this evaluation 

does not include objects located behind the distractor signs or after turns). Table 119 shows the 

ANOVA results. These evaluations found that object type, age group, and previous sign 

assembly affect daytime object detection distance. 
132

Table 118. Model for daytime object detection distance focusing on upstream assembly.

a

Variable Value Std. error DF t-value p-value

Reference Levelb 404.6981 56.24747 220 7.194955 0.0000

Object: box -323.8748 50.61636 220 -6.398619 0.0000

Object: can -23.9229 53.96064 220 -0.443339 0.6580

O. Y: young 296.6900 71.83064 33 4.130410 0.0002

Prev. Assembly: C-A12 133.9378 62.79891 220 2.132804 0.0340

Prev. Assembly: C-B12 154.7027 54.44614 220 2.841389 0.0049

Prev. Assembly: C-B8 166.9417 62.84257 220 2.656507 0.0085

Prev. Assembly: C-V12 77.9409 52.84642 220 1.474857 0.1417

Prev. Assembly: R-A 156.6303 51.24030 220 3.056779 0.0025

Prev. Assembly: LEDs -25.3392 72.64733 220 -0.348798 0.7276

Sign.Fam: Other Common 120.6914 61.33491 220 1.967744 0.0504

Sign.Fam: Uncommon -28.8649 49.59985 220 -0.581956 0.5612

Object: box X O.Y: young -197.6446 70.31444 220 -2.810868 0.0054

Object:can X O.Y: young 64.3989 84.82628 220 0.759186 0.4486 a

Notes:

1. Linear mixed-effects model fit by REML.

2. Fixed effects: Adj_Dis ~ Object * O.Y + Prev.Assembly + Sign.Fam. b

Reference levels in the model has the following conditions:

• Object = pedestrian.

• O.Y. (age: old or young) = older driver.

• Prev. Assembly = R-B.

• Sign.Fam (sign family) = common signs.

Table 119. ANOVA results for daytime detection distance focusing on upstream assembly.

Variable numDF denDF F-value p-value

(Intercept) 1 220 254.85467 < .0001

Object 2 220 77.19983 < .0001

O.Y 1 33 14.81887 0.0005

Prev.Assembly 6 220 4.03653 0.0007

Sign.Fam 2 220 2.62041 0.0750

Object X O.Y 2 220 6.56765 0.0017

Because the interaction between age and object type was significant (as shown in table 118), the 

pair comparisons shown in table 120 include all the possible combinations of object type and 

age. As expected, detection distances are significantly shorter for detecting a box. Older drivers 

detected a pedestrian 324 ft upstream compared with seeing a box behind the assembly, and an 

additional 300 ft for a trash can. Similarly, young drivers recognized a pedestrian 522 ft earlier 

than a box, and a can 562 ft earlier than a box. There is not sufficient evidence to suggest the 

detection distance for pedestrians and trash cans differed, either among old drivers (p-value of 

0.997) or among young drivers (p-value of 0.993). However, detection distances by older drivers 

were significantly shorter compared with younger drivers, except when detecting boxes. Young 

drivers detected a box further upstream of the object compared with older drivers; however, the 

finding was not statistically significant. Young drivers outperformed old drivers by 300 ft when 

detecting a pedestrian and by 361 ft when detecting a trash can. 
133

Table 120. Daytime object detection distance, multiple comparisons by object type and age 

group. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

Box (Old) - Ped (Old) = 0 -323.87 50.62 -6.399 < 0.001***

Can (Old) - Ped (Old) = 0 -23.92 53.96 -0.443 0.997

Can (Old) - Box (Old) = 0 299.95 59.24 5.064 < 0.001***

Box (Young) - Ped (Young) = 0 -521.52 55.00 -9.482 < 0.001***

Can (Young) - Ped (Young) = 0 40.48 75.61 0.535 0.993

Can (Young) - Box (Young) = 0 562.00 69.61 8.074 < 0.001***

Young (Ped) - Old (Ped) = 0 296.69 71.83 4.130 < 0.001***

Young (Box) - Old (Box) = 0 99.05 67.69 1.463 0.624

Young (Can) - Old (Can) = 0 361.09 82.18 4.394 < 0.001*** a Simultaneous tests for general linear hypotheses.

b Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p-values reported single-step 

method.

For daytime object detection distance, the legend on the sign was not significant. As shown in

table 121, no significant differences were identified in the comparison of the Ped X-ing sign face 

with other common signs or to uncommon signs.

Table 121. Daytime object detection distance, multiple comparisons by sign family. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(Ped X-ing) – (Other Common) = 0 -120.69 61.33 -1.968 0.112

(Ped X-ing) – (Uncommon) = 0 28.86 49.60 0.582 0.820

(Uncommon) – (Other Common) = 0 -149.56 79.94 -1.871 0.138 a

Simultaneous tests for general linear hypotheses.

b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p-values reported single-step 

method.

The characteristics of the beacons/LEDs did influence some of the object detection distances as 

shown in table 122 and table 123. Objects were detected at a greater distance upstream when 

located beyond the C-B12, C-B8, and R-A assemblies compared with R-B. During daytime 

conditions, the object detection distance was between 155 to 167 ft longer for the C-B12, C-B8, 

and R-A assemblies compared with the R-B assembly. 

Table 122. Daytime object detection distance, multiple comparisons to reference assembly 

by assembly type. 

Linear Hypothesesa Estimate Std. error z-value Pr(>|z|) b

(C-A12) - (R-B) = 0 133.94 62.80 2.133 0.1656

(C-B12) - ( R-B ) = 0 154.70 54.45 2.841 0.0253*

(C-B8) - (R-B) = 0 166.94 62.84 2.657 0.0435*

(C-V12) - (R-B) = 0 77.94 52.85 1.475 0.5467

(R-A) - (R-B) = 0 156.63 51.24 3.057 0.0129*

(LEDs) - (R-B) = 0 -25.34 72.65 -0.349 0.9993 a

Simultaneous tests for general linear hypotheses.

b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p-values reported single-step 

method.
134

Table 123. Daytime object detection distance, other multiple comparisons by assembly 

type. 

Linear Hypothesesa Estimate Std. error z-value Pr(>|z|) b

(C-B12) - (C-A12) = 0 20.765 58.518 0.355 0.999

(C-B8 ) - (C-A12) = 0 33.004 81.692 0.404 0.999

(C-V12) - (C-A12) = 0 -55.997 75.149 -0.745 0.975

(R-A) - (C-A12) = 0 22.692 62.378 0.364 0.999

(LEDs ) - (C-A12) = 0 -159.277 83.685 -1.903 0.389

(C-B8 ) - (C-B12) = 0 12.239 72.839 0.168 1.000

(C-V12) - (C-B12) = 0 -76.762 66.221 -1.159 0.850

(R-A) - (C-B12) = 0 1.928 57.779 0.033 1.000

(LEDs) - (C-B12) = 0 -180.042 79.316 -2.270 0.199

(C-V12) - (C-B8) = 0 -89.001 60.333 -1.475 0.670

(R-A) - (C-B8) = 0 -10.311 73.408 -0.140 1.000

(LEDs) - (C-B8) = 0 -192.281 82.307 -2.336 0.173

(R-A) - (C-V12) = 0 78.689 61.727 1.275 0.791

(LEDs) - (C-V12) = 0 -103.280 81.212 -1.272 0.792

(LEDs) - (R-A) = 0 -181.969 78.915 -2.306 0.184 a

Simultaneous tests for general linear hypotheses.

b

Adjusted p-values reported – single-step method.

Table 124 shows the comparisons regarding beacon shape and placement with none being 

significant. The daytime distance to detect an object located beyond a study assembly was 

similar for the circular and rectangular beacon shape. The distances were also similar when the 

beacons were located above the sign compared with when the beacons were located below the 

sign. The comparison of the 12-inch beacon to the 8-inch beacon is contained in table 123 with 

the result being no significant difference in daytime detection distance to the object.

Table 124. Daytime object detection distance for beacon placement.

Linear Hypothesesa Estimate Std. error z-value Pr(>|z|) b

(Circular) - (Rectangular) = 0 73.54 39.30 1.871 0.142

(Above) - (Below) [All] = 0 38.08 43.61 0.873 0.679

(Above) - (Below) [Without B8] = 0 67.93 40.24 1.688 0.206 a

Simultaneous tests for general linear hypotheses. b

Adjusted p-values reported – single-step method.

Nighttime Object Detection Distance

Table 125 and table 126 show the results regarding nighttime object detection distance when 

considering upstream conditions (i.e., this evaluation includes objects located behind distractor 

signs and after turns). Unlike daytime conditions, there was not sufficient evidence to suggest a 

difference in detection distance between younger and older drivers. Similar to daytime 

conditions, detection distance differences exist for the object type and for upstream conditions. 

Even though they were statistically significant, the detection distance differences at night were 

smaller and may not be of practical meaningfulness. 

The box was detected 23 ft prior to the pedestrian (statistically significant), and the trash can was 

detected 9 ft prior to the pedestrian (not statistically significant). A significant difference was not 

identified between the detection distance for the objects beyond the distractor signs and the 

objects beyond the signs with beacons or LEDs. A difference was identified for the distance to 
135

the object after a turn (i.e., when no sign is present) and objects following a sign with beacons or 

LEDs. When participants did not have to identify a sign’s presence and to read the sign face, 

they were able to detect objects an additional 31 ft upstream. 

Table 125. Model for nighttime object detection distance considering upstream condition.

a

Variable Value Std. error DF t-value p-value

Reference Levelb 63.08422 6.492316 505 9.716751 0.0000

Object: box 22.53531 5.550685 505 4.059915 0.0001

Object: can 9.47397 5.274080 505 1.796327 0.0730

O.Y: young 0.84664 7.654252 34 0.110610 0.9126

Upstream2: Distractor -2.70914 4.609055 505 -0.587787 0.5569

Upstream2: Turn 31.02125 8.375902 505 3.703631 0.0002 a

Notes:

1. Linear mixed-effects model fit by REML.

2. Fixed effects: Adj_Dis ~ Object + O.Y + Upstream2. b

Reference level in the model has the following conditions:

• Object = pedestrian.

• O.Y. (age: old or young) = older driver.

• Upstream2 = assembly.

Table 126. ANOVA results for nighttime object detection distance considering upstream.

condition.

Variable numDF denDF F-value p-value

(Intercept) 1 505 388.7771 < .0001

Object 2 505 15.4687 < .0001

O.Y 1 34 0.0004 0.9832

Upstream2 2 505 8.1229 0.0003

Table 127 and table 128 show the results when focusing on the type of beacon/LED of the 

previous assembly. The base condition is older participant, with the object being a pedestrian 

located beyond an R-B with a Ped X-ing sign. As shown in table 127 and confirmed with the 

simultaneous tests shown in table 129, age was not a significant variable regarding object 

detection distance at night. The results in table 127 imply significant differences in detection 

distance by type of object; however, the pair comparisons in table 130 demonstrate that the only 

significant difference was between the trash can and the box with an estimated distance 

difference of 24 ft. 
136

Table 127. Model for nighttime object detection distance considering upstream assembly.

a

Variable Value Std. error DF t-value p-value

Reference Levelb 64.68411 10.206352 194 6.337632 0.0000

Object: box 10.54916 7.423142 194 1.421118 0.1569

Object: can -13.00810 7.951071 194 -1.636019 0.1035

O.Y: young 13.46431 9.203264 34 1.462993 0.1527

Prev.Assembly: C-A12 17.79469 10.677269 194 1.666596 0.0972

Prev.Assembly: C-B12 35.22210 11.293281 194 3.118854 0.0021

Prev.Assembly: C-B8 23.19978 12.498281 194 1.856238 0.0649

Prev.Assembly: C-V12 22.16982 12.481922 194 1.776154 0.0773

Prev.Assembly: R-A 23.26325 11.247088 194 2.068380 0.0399

Prev.Assembly: LEDs 24.52451 15.412787 194 1.591179 0.1132

Sign.Fam: Other Common -31.77985 11.438550 194 -2.778311 0.0060

Sign.Fam: Uncommon -22.72245 9.561413 194 -2.376474 0.0185 a

Notes:

1. Linear mixed-effects model fit by REML.

2. Fixed effects: Adj_Dis ~ Object + O.Y + Prev.Assembly + Sign.Fam. b

Reference level in the model has the following conditions:

• Object = pedestrian.

• O.Y. (age: old or young) = older driver.

• Prev.Assembly = R-B.

• Sign.Fam (sign family) = common signs.

Table 128. ANOVA results using for nighttime object detection distance considering 

upstream assembly.

Variable numDF denDF F-value p-value

(Intercept) 1 194 257.49711 < .0001

Object 2 194 3.61435 0.0288

O.Y 1 34 1.47261 0.2333

Prev.Assembly 6 194 2.46570 0.0254

Sign.Fam 2 194 6.27471 0.0023

Table 129. Nighttime object detection distance, multiple comparisons by age group. 

Linear hypothesesa Estimate Std. error z value Pr(>|z|) b

Young - Old = 0 13.464 9.203 1.463 0.143 a

Simultaneous tests for general linear hypotheses. b

Adjusted p values reported single-step method.

Table 130. Nighttime object detection distance, multiple comparisons by object type. 

Linear hypothesesa Estimate Std. error z value Pr(>|z|) b

(Ped X-ing) - (Other Common) = 0 31.780 11.439 2.778 0.0143*

(Ped X-ing) - (Uncommon) = 0 22.722 9.561 2.376 0.0437*

(Uncommon) - (Other Common) = 0 9.057 14.416 0.628 0.7976 a

Simultaneous tests for general linear hypotheses.

b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Detecting the object occurred at slightly greater distances when beyond the signs showing Ped 

X-ing compared with uncommon signs (23 ft) or other common signs (32 ft) as shown in

table 131.
137

Table 131. Nighttime object detection distance, multiple comparisons by sign family. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(Ped X-ing) - (Other Common) = 0 31.780 11.439 2.778 0.0143*

(Ped X-ing) - (Uncommon) = 0 22.722 9.561 2.376 0.0437*

(Uncommon) - (Other Common) = 0 9.057 14.416 0.628 0.7976 a

Simultaneous tests for general linear hypotheses.

b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Table 132 and table 133 are pair comparisons between different beacon/LED types. Only one 

paired comparison had a statistically significant result (C-B12 to R-B). In all other cases, the 

detection distance to the object was not significantly different among the various beacon/LED 

assemblies. During nighttime conditions, objects were detected 35 ft farther upstream with the 

C-B12 than with the R-B. The objects were also detected at a greater distance upstream during 

the daytime with C-B12 compared with R-B (155 ft). 

Table 134 shows the comparisons regarding beacon shape and placement with none being 

significant. The distance to detect an object located beyond a study assembly was similar for the 

circular and rectangular beacon shape. The distances were also similar when the beacons were 

located above the sign compared with when the beacons were located below the sign. The 

comparison of the 12-inch beacon to the 8-inch beacon is contained in table 133 with the result 

being no significant difference in detection distance to the object.

Table 132. Nighttime object detection distance, multiple comparisons to reference assembly 

by assembly type. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(C-A12) - (R-B) = 0 17.79 10.68 1.667 0.4148

(C-B12) - (R-B) = 0 35.22 11.29 3.119 0.0106*

(C-B8 ) - (R-B) = 0 23.20 12.50 1.856 0.2969

(C-V12) - (R-B) = 0 22.17 12.48 1.776 0.3440

(R-A) - (R-B) = 0 23.26 11.25 2.068 0.1928

(LEDs) - (R-B) = 0 24.52 15.41 1.591 0.4672 a

Simultaneous tests for general linear hypotheses.

b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.
138

Table 133. Nighttime object detection distance, other multiple comparisons by assembly 

type. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(C-B12) - (C-A12) = 0 17.42741 12.66434 1.376 0.732

(C-B8 ) - (C-A12) = 0 5.40509 15.39298 0.351 0.999

(C-V12) - (C-A12) = 0 4.37513 15.21513 0.288 1.000

(R-A) - (C-A12) = 0 5.46856 12.06913 0.453 0.997

(LEDs) - (C-A12) = 0 6.72982 17.24016 0.390 0.999

(C-B8) - (C-B12) = 0 -12.02232 14.80255 -0.812 0.964

(C-V12) - (C-B12) = 0 -13.05228 14.21682 -0.918 0.939

(R-A) - (C-B12) = 0 -11.95885 13.09877 -0.913 0.940

(LEDs ) - (C-B12) = 0 -10.69759 17.28384 -0.619 0.989

(C-V12) - (C-B8) = 0 -1.02996 11.39493 -0.090 1.000

(R-A) - (C-B8) = 0 0.06347 15.31347 0.004 1.000

(LEDs) - (C-B8) = 0 1.32473 16.81555 0.079 1.000

( R-A) - (C-V12) = 0 1.09343 14.83082 0.074 1.000

(LEDs) - (C-V12) = 0 2.35469 17.25943 0.136 1.000

(LEDs) - (R-A) = 0 1.26126 17.64379 0.071 1.000 a

Simultaneous tests for general linear hypotheses.

b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Table 134. Nighttime object detection distance for beacon placement.

Linear hypothesesa Estimate Std. error z-value Pr(>|z|)

(Circular) - (Rectangular) = 0 13.773 7.624 1.807 0.156

(Above) - (Below) [All] = 0 1.057 9.038 0.117 0.998

(Above) - (Below) [Without B8] = 0 2.918 8.650 0.337 0.960 a

Simultaneous tests for general linear hypotheses.

Key Findings Regarding Object Detection Distance

For the analysis focusing on object detection distance, which is the distance between the object 

and the participant when the participant says “ped”, “can,” or “box,” results indicate the 

following:

• As expected, there is a significant difference between daytime and nighttime object 

detection distance. As an example, the daytime detection distance to a pedestrian had 

a mean of 911 ft and a standard deviation of 539 ft. In contrast, during the night, the 

detection distance to a pedestrian had a mean of only 116 and a standard deviation of 

93 ft.

• Similar to legibility distance, there was a statistically significant difference owing to 

age during the daytime; surprisingly, the same finding did not occur at night. The 

nighttime condition itself seems to impede detection to a point that the effects of 

several variables are too small to detect in the experiment.

• The detection distance to a pedestrian or to a trash can was similar for both daytime 

and nighttime conditions. While the pedestrians were a few feet taller than the can 

cans and had a similar width, they were much larger than the small box. As expected, 

the detection distance to a box compared with the pedestrian was shorter during the 
139

daytime by 324 ft (older drivers) or 522 ft (younger drivers). Surprisingly, the 

nighttime detection distance to the box was slightly longer (by about 24 ft) compared 

with the can. 

• Certain assemblies were associated with shorter object detection distances. For 

daytime conditions, the detection distance to an object was shorter for the R-B than 

with the C-B12, C-B8, or the R-A (statistically significant). During the nighttime, the 

detection distance to an object was statistically significantly shorter with the R-B than 

with the C-B12. These findings indicate that characteristics of the R-B, such as the 

light intensity or the location of the beacon beneath the sign, may negatively affect 

driver’s ability to see an object beyond the assembly.

• For both daytime and nighttime conditions, similar object detection distance results 

regardless of the shape of the beacons (circular or rectangular) or the placement of the 

beacons (above or below).

• Based on the above results and similar to legibility distance recommendation, the 

suggested alternative for testing in the on-road study is the C-B12 assembly.

Accuracy of Detecting an Object

The previous section discussed the detection distance to an object that was identified. The 

analysis did not consider when a participant missed the object; in other words, situations where 

the participant did not say “box,” “ped,” or “can” when that object was present. This section 

identifies factors that contribute to drivers missing an object and examines the influence of the 

preceding assembly on whether an object was missed.

The evaluation of the accuracy of detecting an object considered whether the presence of a sign 

affected a participant missing an object. Similar to the evaluation on object detection distance, 

for this evaluation, the following variables were considered:

• Time (day or night).

• Age (young or old).

• Object type (box, trash can, or pedestrian).

• Upstream2 (grouped by signs with Beacon-LED, distractor signs, and turn. The turn 

group represents the situation when the object was not located beyond a sign. This 

variable does not include the WO-B device because it only had one object—the 

box—that followed the sign.). 

The evaluation then continued with focusing on those situations when an object followed a sign 

that had some type of beacon or LED. For this evaluation, the following variables were 

considered:
140

• Time (day or night).

• Age (young or old).

• Object type (box, can, or ped).

• Previous device (C-A12, C-B12, C-B8, C-V12, R-B, R-A, or LED).

• Previous sign family (Ped X-ing, common signs, and uncommon signs).

To provide an appreciation of the results, table 135 and table 136 show the frequency (count) of 

objects detected and missed along with the percent missed. The results in table 135 show 

differences in percent missed by object type, by upstream condition, and by light level. More 

objects were missed during nighttime conditions: 21 percent of the boxes, 21 percent of the trash 

cans, and 21 percent of the pedestrians. Said in another way, about one in five objects were 

missed at night. During the daytime, fewer pedestrians and trash cans were missed; however, 

more boxes were missed. Table 136 shows similar data with a focus on age of the participant. 

Older drivers missed more of the objects (26 percent overall) compared with younger drivers 

(12 percent overall). Because other factors are known to affect the results of detection distances, 

the following statistical evaluation indicates whether these preliminary observations are 

statistically significant.

Table 135. Object percent missed by previous device group, object type, and light 

condition.

Action

Previous device 

group

Day Night Total Box Can Ped Total Box Can Ped Total

Miss

Sign with BeaconLED 27 11 2 40 19 28 29 76 116

Distractor 41 7 0 48 30 23 15 68 116

Turn 44 1 0 45 24 7 0 31 76

WO-B 7 0 0 7 3 0 0 3 10

Total 119 19 2 140 76 58 44 178 318

Saw

Sign with BeaconLED 80 92 138 310 89 75 111 275 585

Distractor 62 97 69 228 77 81 55 213 441

Turn 56 69 0 125 81 65 0 146 271

WO-B 27 0 0 27 33 0 0 33 60

Total 225 258 207 690 280 221 166 667 1357

Percent

Sign with BeaconLED 25% 11% 1% 11% 18% 27% 21% 22% 17%

Distractor 40% 7% 0% 17% 28% 22% 21% 24% 21%

Turn 44% 1% NA 26% 23% 10% NA 18% 22%

WO-B 21% NA NA 21% 8% NA NA 8% 14%

All 35% 7% 1% 17% 21% 21% 21% 21% 19%

Ped = Pedestrian.

NA = percentage not calculated because no events with those properties occurred.
141

Table 136. Object percent missed by age group, object type, and light condition.

Action Age

Day Night Total Box Can Ped Total Box Can Ped Total

Miss

Old 78 15 2 95 56 38 25 119 214

Young 41 4 0 45 20 20 19 59 104

Both 119 19 2 140 76 58 44 178 318

Saw

Old 95 126 106 327 111 93 71 275 602

Young 130 132 101 363 169 128 95 392 755

Both 225 258 207 690 280 221 166 667 1357

Percent

Old 45% 11% 2% 23% 34% 29% 26% 30% 26%

Young 24% 3% 0% 11% 11% 14% 17% 13% 12%

Both 35% 7% 1% 17% 21% 21% 21% 21% 19%

NA = percentage not calculated because no events with those properties occurred.

Because the data structure is the same for the percent miss and object detection distance 

variables, the blocking structure for this analysis was preserved (i.e., light condition/driver/lap 

number). The data were then split by daytime and nighttime conditions so that each set could be 

analyzed using Generalized Linear Mixed Effects Models (GLMM). These kinds of models are 

very similar to LMMs in that they treat random effects as blocking factors, and the fixed effects 

as parameter estimates. The key difference is that because the response variable is binary (1 if 

the object was missed, 0 otherwise), the estimation requires specifying a link function (logistic 

function in this case) as Generalized Linear Models also do. Weights were not applied in this 

case. 

Daytime Object Detection Accuracy

Table 137 shows the results regarding detecting objects during the daytime when considering 

upstream conditions (i.e., this evaluation includes objects located behind distractor signs and 

after turns). The probability of detecting an object was statistically significantly different for the 

age groups (older drivers were more likely to miss the object) and object type (participants were 

more likely to miss a box or a trash can than the pedestrian). The upstream condition was not 

statistically significant. In other words, it did not matter whether the object was beyond a sign 

with beacons or LEDs or beyond one of the detector signs or not beyond a sign (i.e., following a 

turn). The probability of missing an object was similar regardless of the upstream condition. 

Table 138 shows the results when focusing on the type of beacon/LED of the previous assembly. 

The base condition is older participant, with the object being a pedestrian located beyond an R-B 

with a Ped X-ing sign. Similar to the findings in table 137, age group and object type were 

significant. The findings in table 138 also show that the probability of missing an object is 

sensitive to the type of beacons/LED when compared with the base condition of R-B. R-A was 

the only previous assembly that was not significantly different. 
142

Table 137. Model for daytime object detection accuracy considering upstream condition.

a

Variables Value Std. error DF t-value p-value

Reference Levelb -5.065441 0.6706133 721 -7.553446 0.0000

O.Y: young -1.040132 0.4196777 33 -2.478406 0.0185

Object: box 4.621094 0.6284863 721 7.352736 0.0000

Object: can 2.105317 0.6404133 721 3.287434 0.0011

Upstream2: Distractor 0.450288 0.2384175 721 1.888652 0.0593

Upstream2: Turn 0.303752 0.2534788 721 1.198334 0.2312 a

Notes:

1. Linear mixed-effects model fit by maximum likelihood.

2. Fixed effects: Miss ~ O.Y + Object + Upstream2. b

Reference level in the model has the following conditions:

• O.Y. (age: old or young) = older driver.

• Object = pedestrian.

• Upstream2 = assembly.

Simultaneous tests were conducted to determine whether differences exist for selected

comparisons. Results are shown in table 138 to table 142.

The model results confirm a statistically significant difference by object type (see table 138),

with both box and trash can being more likely to be missed than a pedestrian. In addition, the box 

was more likely to be missed than the trash can. These results are expected because the box is a 

much smaller target than either the trash can or the pedestrian. Young drivers were less likely to 

miss objects than old drivers as shown in table 139.

After controlling for other factors, there was no evidence of different probability of missing an 

object following the Ped X-ing and other common signs. Strong evidence is present for the odds 

of missing objects after uncommon signs as shown in table 140; the added challenge of reading 

and interpreting uncommon signs may have resulted in more drivers missing objects behind 

them.

Table 141 shows the results for the multiple assembly comparisons when each assembly is 

compared with the R-B. Table 142 provides the results between the different assemblies. The 

results indicate several pairs of devices that were associated with the participants being more 

likely to miss an object during the daytime. Recall, as shown in table 135, the object most likely 

missed was the small box.

Table 144 shows the results when examining the effects of beacon shape and placement. The 

shape of the beacons did not result in statistically significant differences; a similar probability of 

missing the object was present whether the beacons were circular or rectangular. If the beacons 

were located above or below the sign did matter; when beacons were above the sign, the 

participants were less likely to miss the object. The same results were found whether the below 

group included all beneath beacons (i.e., R-B, C-B12, and C-B8) or only those beacons beneath 

the sign that had a direct match with above beacons (i.e., R-B and C-B12 compared with R-A 

and C-A12).

The size of the circular beacon did not have an effect of the probability of missing an object. As 

shown in table 143, the difference between C-B8 and C-B12 was not statistically significant. 
143

Table 138. Model for daytime object detection accuracy considering upstream assembly.

a

Variables Value Std. error DF t-value p-value

Reference Levelb -31.036509 .429661 270 -9.049439 0.0000

O.Y: young -7.604175 3.355508 33 -2.266177 0.0301

Object: box 22.528656 2.060295 270 10.934673 0.0000

Object: can 12.075817 1.600502 270 7.545019 0.0000

Prev.Assembly: C-A12 -5.471284 1.604065 270 -3.410887 0.0007

Prev.Assembly: C-B12 3.308869 1.139095 270 2.904824 0.0040

Prev.Assembly: C-B8 7.405977 1.412711 270 5.242386 0.0000

Prev.Assembly: C-V12 1.985354 0.896321 270 2.215004 0.0276

Prev.Assembly: R-A 1.363892 1.116743 270 1.221312 0.2230

Prev.Assembly: LEDs 13.164725 1.781986 270 7.387669 0.0000

Sign.Fam: Other Common -1.117426 1.433653 270 -0.779426 0.4364

Sign.Fam: Uncommon 17.846180 1.817777 270 9.817586 0.0000 a

Notes:

1. Linear mixed-effects model fit by maximum likelihood.

2. Fixed effects: Miss ~ O.Y + Object + Prev.Assembly + Sign.Fam. b

Reference level in the model has the following conditions:

• O.Y. = older driver.

• Object = pedestrian.

• Prev.Assembly = R-B.

• Sign.Fam = common.

Table 139. Daytime object detection accuracy, multiple comparisons by object type. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

Box - Ped = 0 22.529 2.025 11.127 < 1e-10***

Can - Ped = 0 12.076 1.573 7.678 < 1e-10***

Can - Box = 0 -10.453 1.385 -7.545 < 1e-10*** a

Simultaneous tests for general linear hypotheses. b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Table 140. Daytime object detection accuracy, multiple comparisons by age group.

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

Young - Old = 0 -7.604 3.297 -2.306 0.0211 * a

Simultaneous tests for general linear hypotheses. b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Table 141. Daytime object detection accuracy, multiple comparisons by sign family. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(Ped X-ing) - (Other Common) = 0 1.117 1.409 0.793 0.681

(Ped X-ing) - (Uncommon) = 0 -17.846 1.786 -9.990 < 1e-04***

(Uncommon) - (Other Common) = 0 18.964 2.498 7.591 < 1e-04*** a Simultaneous tests for general linear hypotheses. b Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.
144

Table 142. Daytime object detection accuracy, multiple comparisons to reference assembly 

by assembly type.

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(C-A12) - ( R-B ) = 0 -5.4713 1.5763 -3.471 0.00277**

(C-B12) - ( R-B ) = 0 3.3089 1.1194 2.956 0.01564 *

(C-B8 ) - ( R-B ) = 0 7.4060 1.3883 5.335 < 0.001***

(C-V12) - ( R-B ) = 0 1.9854 0.8808 2.254 0.10620

( R-A ) - ( R-B ) = 0 1.3639 1.0974 1.243 0.63116

(LEDs) - (R-B ) = 0 13.1647 1.7512 7.518 < 0.001*** a

Simultaneous tests for general linear hypotheses. b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Table 143. Daytime object detection accuracy, other multiple comparisons by assembly 

type. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(C-B12) - (C-A12) = 0 8.7802 1.2159 7.221 < 0.001***

(C-B8 ) - (C-A12) = 0 12.8773 2.0258 6.356 < 0.001***

(C-V12) - (C-A12) = 0 7.4566 1.7470 4.268 < 0.001***

( R-A ) - (C-A12) = 0 6.8352 1.1431 5.980 < 0.001***

(LEDs) - (C-A12) = 0 18.6360 1.8148 10.269 < 0.001***

(C-B8 ) - (C-B12) = 0 4.0971 1.7388 2.356 0.14048

(C-V12) - (C-B12) = 0 -1.3235 1.3524 -0.979 0.89861

( R-A ) - (C-B12) = 0 -1.9450 0.5526 -3.520 0.00437**

(LEDs ) - (C-B12) = 0 9.8559 1.5051 6.548 < 0.001***

(C-V12) - (C-B8 ) = 0 -5.4206 1.1426 -4.744 < 0.001***

( R-A ) - (C-B8 ) = 0 -6.0421 1.7680 -3.417 0.00664**

(LEDs ) - (C-B8 ) = 0 5.7587 1.2991 4.433 < 0.001***

( R-A ) - (C-V12) = 0 -0.6215 1.3740 -0.452 0.99638

(LEDs ) - (C-V12) = 0 11.1794 1.6652 6.714 < 0.001***

(LEDs ) - ( R-A ) = 0 11.8008 1.4812 7.967 < 0.001*** a

Simultaneous tests for general linear hypotheses. b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Table 144. Daytime object detection accuracy for beacon placement.

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(Circular) - (Rectangular) = 0 1.0659 0.6903 1.544 0.257

(Above) - (Below) [All] = 0 -5.6250 1.0494 -5.360 < 0.001***

(Above) - (Below) [Without B8] = 0 -3.7081 0.8534 -4.345 < 0.001*** a

Simultaneous tests for general linear hypotheses. b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Nighttime Object Detection Accuracy

Table 145 shows the results regarding detecting objects during the nighttime when considering 

upstream conditions (i.e., this evaluation includes objects located behind distractor signs and 

after turns). The probability of detecting an object was statistically significantly different for age 

groups (older drivers were more likely to miss the object). While object type was statistically 

different during the daytime, it was not during the nighttime. In other words, the probability of 

missing any of the different objects was similar at night. 
145

Similar to daytime conditions, the upstream condition was not significant. In other words, it did 

not matter whether the object was beyond a sign with beacons or LEDs or beyond one of the 

detector signs or not beyond a sign (i.e., following a turn). The probability of missing the object 

was similar regardless of the upstream condition. 

Table 146 shows the results when focusing on the type of beacon/LED of the previous assembly. 

The base condition is older participant, with the object being a pedestrian located beyond an R-B 

with a Ped X-ing sign. Similar to the findings in table 145, age group was significant while 

object type was not. The pair comparison shown in table 147 confirms that the probability of 

missing an object at night was similar for the three objects. Table 148 confirms the findings that 

age does affect the probability of missing an object. No statistical difference was identified 

between the different sign families (Ped X-ing, other common signs, and uncommon signs) as 

shown in table 149. 

Table 150 and table 151are pair comparisons between different beacon/LED types. The only 

comparisons with a statistical difference involved the sign with the embedded LEDs. Care should 

be taken with this finding because only one object type was present beyond the LED-embedded 

signs (the trash can) and the LED-embedded signs were not rotated to other positions on the 

course because of study limitations.

The comparison between the beacon shape (circular or rectangular) revealed no difference in the 

probability of missing an object (see table 152). The comparisons regarding beacon placement 

(above or below the sign) also showed no difference. In other words, the shape and location of 

the beacon within the sign assembly did not affect the probability of missing an object. Recall 

that the probability of missing any of the objects at night is higher than during the day. 

Table 145. Model for nighttime object detection accuracy considering upstream condition.a

Variables Value Std. error DF t-value p-value

Reference Levelb -1.19514 0.3381 732 -3.535173 0.0004

O.Y: young -1.20987 0.4271 34 -2.83279 0.0077

Object: box 0.269929 0.2202 732 1.22575 0.2207

Object: can 0.086253 0.2247 732 0.383848 0.7012

Upstream2: Distractor 0.178722 0.1867 732 0.957376 0.3387

Upstream2: Turn -0.40148 0.2404 732 -1.669795 0.0954 a

Notes:

1. Linear mixed-effects model fit by maximum likelihood.

2. Fixed effects: Miss ~ O.Y + Object + Upstream2. b

Reference level in the model have the following conditions:

• O.Y. = older driver.

• Object = pedestrian.

• Upstream2 = assembly.
146

Table 146. Model for nighttime object detection accuracy considering upstream assembly.

a

Variables Value Std. error DF t-value p-value

Reference Levelb -2.04057 0.53165 269 -3.83818 0.0002

O.Y: young -1.14365 0.460748 34 -2.48216 0.0182

Object: can 0.547958 0.417624 269 1.312085 0.1906

Object: ped 0.396126 0.334421 269 1.184514 0.2373

Prev.Assembly: C-A12 -0.063 0.539061 269 -0.11686 0.9071

Prev.Assembly: C-B12 0.402231 0.477727 269 0.841968 0.4006

Prev.Assembly: C-B8 0.212159 0.58904 269 0.360178 0.7190

Prev.Assembly: C-V12 0.499291 0.569511 269 0.876701 0.3814

Prev.Assembly: R-A 0.955322 0.469532 269 2.034627 0.0429

Prev.Assembly: LEDs 1.888127 0.554063 269 3.407785 0.0008

Sign.Fam: Other Common -0.51713 0.639166 269 -0.80907 0.4192

Sign.Fam: Uncommon 0.558717 0.403549 269 1.384508 0.1674 a

Notes:

1. Linear mixed-effects model fit by maximum likelihood.

2. Fixed effects: Miss ~ O.Y + Object + Prev.Assembly + Sign.Fam b

Reference level in the model have the following conditions:

• O.Y. = older driver.

• Object = pedestrian.

• Prev.Assembly = R-B.

• Sign.Fam = common.

Table 147. Nighttime object detection accuracy, multiple comparisons by object type. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|)

Box - Ped = 0 -0.3961 0.3287 -1.205 0.447

Can - Ped = 0 0.1518 0.3724 0.408 0.912

Can - Box = 0 0.548 0.4104 1.335 0.373 a

Simultaneous tests for general linear hypotheses.

Table 148. Nighttime object detection accuracy, multiple comparisons by age group.

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

Young - Old = 0 -1.1437 0.4528 -2.526 0.0115* a

Simultaneous tests for general linear hypotheses. b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported—single-step 

method.

Table 149. Nighttime object detection accuracy, multiple comparisons by sign family. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|)

(Ped X-ing) - (Other Common) = 0 0.5171 0.6281 0.823 0.671

(Ped X-ing) - (Uncommon) = 0 -0.5587 0.3966 -1.409 0.317

(Uncommon) - (Other Common) = 0 1.0758 0.7619 1.412 0.315 a

Simultaneous tests for general linear hypotheses.
147

Table 150. Nighttime object detection accuracy, multiple comparisons to reference 

assembly by assembly type. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(C-A12) - ( R-B ) = 0 -0.063 0.5298 -0.119 1.0000

(C-B12) - ( R-B ) = 0 0.4022 0.4695 0.857 0.9132

(C-B8 ) - ( R-B ) = 0 0.2122 0.5789 0.366 0.99877

(C-V12) - ( R-B ) = 0 0.4993 0.5597 0.892 0.89772

( R-A ) - ( R-B ) = 0 0.9553 0.4614 2.07 0.17552

(LEDs) - ( R-B ) = 0 1.8881 0.5445 3.468 0.00298** a

Simultaneous tests for general linear hypotheses. b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Table 151. Nighttime object detection accuracy, other multiple comparisons by assembly 

type. 

Linear hypothesesa Estimate Std. error z-value Pr(>|z|) b

(C-B12) - (C-A12) = 0 0.46523 0.47666 0.976 0.9225

(C-B8 ) - (C-A12) = 0 0.27515 0.6768 0.407 0.9985

(C-V12) - (C-A12) = 0 0.56229 0.66282 0.848 0.9564

(R-A) - (C-A12) = 0 1.01832 0.4899 2.079 0.2904

(LEDs) - (C-A12) = 0 1.95112 0.59876 3.259 0.0136*

(C-B8 ) - (C-B12) = 0 -0.19007 0.6011 -0.316 0.9996

(C-V12) - (C-B12) = 0 0.09706 0.57848 0.168 1.0000

(R-A) - (C-B12) = 0 0.55309 0.44805 1.234 0.8137

(LEDs) - (C-B12) = 0 1.4859 0.54986 2.702 0.0721

(C-V12) - (C-B8) = 0 0.28713 0.5532 0.519 0.9952

(R-A) - (C-B8) = 0 0.74316 0.60602 1.226 0.8179

(LEDs) - (C-B8) = 0 1.67597 0.60419 2.774 0.0595

(R-A) - (C-V12) = 0 0.45603 0.56416 0.808 0.9645

(LEDs) - (C-V12) = 0 1.38884 0.61456 2.26 0.2045

(LEDs) - (R-A) = 0 0.93281 0.54366 1.716 0.5117 a

Simultaneous tests for general linear hypotheses. b

Significance values are as follows: p < 0.10; * p < 0.05; ** p < 0.01; and *** p < 0.001. Adjusted p values reported single-step 

method.

Table 152. Nighttime object detection accuracy for beacon placement.

Linear hypothesesa Estimate Std. error z-value Pr(>|z|)

(Circular) - (Rectangular) = 0 -0.2939 0.3233 -0.909 0.657

(Above) - (Below)[All] = 0 0.2414 0.3595 0.671 0.810

(Above) - (Below)[Without B8] = 0 0.2450 0.3395 0.722 0.779 a

Simultaneous tests for general linear hypotheses.

Speed When Object Was Detected or Missed

During reviews of the previous finding, a question was asked on whether the drivers who missed 

the pedestrians and the trash cans were driving faster compared with the drivers who detected the 

objects. The speed when the participant detected the object was readily available. The speed 

when a participant missed the object was not as readily available. The research team searched the 

data stream to identify the speed when the driver who missed an object was near the location of 

the missed object. Table 153 provides the average speeds. When speed was included in the 

statistical evaluation, it was found to be not significant. The drivers who missed the pedestrian or 
148

the trash can during the study were operating the vehicle at similar speeds as the drivers who 

detected the pedestrian or trash can, both in the daytime and the nighttime.

Table 153. Average speed of vehicle when object was detected or missed.

Day or 

night Object

Object missed Object detected

Average 

speed (mi/h)

Standard 

deviation of

speed (mi/h) Count

Average 

speed 

(mi/h)

Standard 

deviation of 

speed (mi/h) Count

Day Can 35 5.4 19 33 8.2 263

Day Ped 37 3.1 2 38 5.9 210

Day Either 36 5.2 21 35 7.7 473

Night Can 32 6.4 58 33 5.7 227

Night Ped 35 4.3 44 34 6.4 167

Night Either 34 5.8 102 34 6.0 394

Key Findings Regarding Object Detection Accuracy

For the analysis focusing on the accuracy of detecting objects, which considered the number of 

objects missed by the participants, results indicate the following:

• As expected, there is a significant difference in the probability of missing objects 

between daytime and nighttime conditions. What was not expected was the 

magnitude of the difference. Overall, during the day, 1 in 23 pedestrians/trash cans 

were missed while at night 1 in 5 pedestrians/trash cans were missed. 

• For both daytime and nighttime conditions, the shape of the beacon did not matter; a 

similar probability of missing the object was present whether the beacons were 

circular or rectangular.

• The drivers who missed the pedestrian or the trash can during the study were 

operating the vehicle at speeds similar to the drivers who detected the pedestrian or 

trash can, both in the daytime and the nighttime. For this study, the speed the 

participant was driving did not affect whether an object was missed.

• The location of the beacons (above or below the sign) was significant during the day 

but not at night. During the day, participants were less likely to miss an object when 

the beacons were above the sign.

Discomfort Glare

Raw Data

Raw data for the discomfort glare study are shown in figure 64 for C-B8 at position 1, figure 65

for C-B8 at position 2, figure 66 for LED at position 1, and figure 67 for LED at position 2. The 

abbreviations for these figures include the following:

• P1 = position 1 or 250 ft in advance of assembly.

• P2 = position 2 or 150 ft in advance of assembly.
149

• C = Comfortable—the glare is not annoying and the signal is easy to look at.

• I = Irritating—the glare is uncomfortable; however, the participant is still able to look 

at it without the urge to look away.

• U = Unbearable—the glare is so intense that the participant wants to avoid looking 

at it.

The data show that for all devices at all distances, the percentage of participants indicating the 

brightness of the lights from the beacons/LEDS was comfortable decreased as brightness 

increased and the percentage of participants indicating the discomfort glare was unbearable 

increased as brightness increased. In addition, the data show that almost 50 percent of all 

participants indicated the discomfort glare was unbearable at a setting number 6 for all devices. 

Figure 64. Graph. Percent of response for discomfort glare study—C-B8 at position 1.

Figure 65. Graph. Percent of response for discomfort glare study—C-B8 at position 2.
150

Figure 66. Graph. Percent of response for discomfort glare study—LED at position 1.

Figure 67. Graph. Percent of response for discomfort glare study—LED at position 2.

Statistical Method

The statistics program R was used to develop cumulative logistic regression models to evaluate 

participant’s ratings of discomfort glare. Cumulative logistic regression models are a method for 

calculating the probability of a response less than or equal to a specific value for ordinal data; the 

discomfort ratings in this study (comfortable, irritating, and unbearable) are ordinal data. The 

basic form of the Cumulative Logit models in this study is shown in figure 68 (32)

:

Figure 68. Equation. Basic form of the Cumulative Logit model.

Where:

Logit(p) = odds(p) = ln(p) – ln(1-p).

P = P(I_ciu ≤ j).

I_ciu = discomfort level, such that I_ciu ∈{1,2,3} → 〈comfortable, irritating, unbearable〉

Ln = the natural log function.

P(I_ciu ≤ j) = probability of I_ciu not exceeding threshold j, such that j ∈ {1,2,3}.

αj = intercept value (for comfortable j = 1, for irritating j = 2).

βi = i

th parameter value.

Xi = i

th variable.

Logit( P(I_ciu ≤ j) ) = αj + βiXi
151

To calculate the probability of event I_ciu = 3 (unbearable brightness), the equation in figure 69

is valid.

Figure 69. Equation. Probability of unbearable brightness.

Where:

P(I_ciu =3) = probability of I_ciu equal 3 (unbearable).

P(I_ciu ≤ 2) = probability of I_ciu ≤ 2 (comfortable or irritating).

α2 = intercept value for P(I_ciu ≤ 2).

βi = i

th parameter value.

Xi = i

th variable value.

Exp = the exponential function.

For variables only taking two values, exp (βi) is the odds ratio, which is the probability of 

event A occurring divided by the probability of event B occurring. Mathematically, the odds 

ratio is as shown in figure 70:

Figure 70. Equation. Basic form of the odds ratio for events A and B.

Where:

Pa = probability of event A occurring.

Pb = probability of event B occurring. 

Model Selection

The variables considered for inclusion in the statistical models are defined in table 154. To select 

statistically significant cumulative logistic regression models, researchers used the Akaike’s 

Information Criterion (AIC) to assess the value of adding a parameter to the model. First, 

researchers tested the three measures of brightness (C_Level, INT_One, and OP_One) to 

determine which parameter had the lowest AIC. Next, researchers tested the non-brightness 

measures for inclusion in the model, adding one parameter at a time, adding the parameter with 

the lowest AIC. Models with lower AIC values fit the data better than models with higher AIC 

values while limiting the number of parameters in the model. To say it another way, the selected 

models maximize log-likelihood while minimizing the number of parameters in the model. 

Researchers did not investigate the inclusion of cross terms in this study. 

P(I_ciu= 3) = 1 – P(I_ciu ≤ 2) = 1 / [ exp(α2 + βiXi) + 1 ]

Odds ratio = [Pa/(1-Pa)] / [Pb/(1-Pb)]
152

Table 154. Discomfort glare variable names and descriptions.

Variable 

name Description

I_ciu Indicates whether the participant felt the device was comfortable, irritating, or unbearable to look at 

for a given combination of variables; equals 1 if comfortable, 2 if irritating, and 3 if unbearable.

INT_One 95th percentile intensity measured at a vertical angle of zero and a horizontal angle of zero.

OP_One Optical power measured at a vertical angle of zero and a horizontal angle of zero.

C_Level Setting on the controller for a given observation; equals 1 through 6 as categorical variables.

M_Num Measurement number representing the order measurements were taken (0 = 250 ft for sign 1, 

1 = 150 ft for sign 1, 2 = 250 ft for sign 2, 3 = 250 ft for sign 2) 

I_Age Indicator value for age of participant; equals 1 for participants 55 years or older, 0 otherwise.

I_Dist Indicator value for distance from assembly where measurements were taken; 1 if 250 ft, 

0 otherwise.

I_Device Indicator value for device where measurements were taken; 1 if sign 1, 0 if sign 2.

I_Day Indicator value for day or night; 1 if day, 0 if night.

After selecting an appropriate cumulative logistic regression model with C_Level as the measure 

of brightness, researchers generated two additional models using OP_One and INT_One as the 

parameters representing the brightness of the device; these models allow researchers to compare 

participants’ rating of discomfort glare with measures of brightness found in SAE standard J595. 

The models evaluated as part of this process and associated AIC values are shown in table 155. 

Note that the only difference between the three selected models is the parameter used to 

represent the brightness of the device. 

Table 155. Model specification and selection using Akaike Information Criterion (AIC).

Modela

Number of

parameters

Loglikelihood AICModel selected for further 

analysis

INT_One 3 -1,696 3,398 No

OP_One 3 -1,563 3,131 No

C_Level 7 -1,421 2,856 Yes

C_Level+M_Num 10 -1,409 2,838 No

C_Level+I_Age 8 -1,418 2,846 No

C_Level+I_Dist 8 -1,415 2,854 No

C_Level+I_Device 8 -1,421 2,857 No

C_Level+I_Day 8 -1,173 2,363 Yes

C_Level+I_Day+M_Num 11 -1,158 2,339 Yes

C_Level+I_Day+I_Age 9 -1,169 2,356 No

C_Level+I_Day+I_Dist 9 -1,172 2,361 No

C_Level+I_Day+I_Device 9 -1,173 2,364 No

C_Level+I_Day+M_Num+I_Age 12 -1,154 2,332 Yes

Selected Models

C_Level+I_Day+M_Num+I_Age 12 -1,154 2,332 Yes

OP_One+I_Day+M_Num+I_Age 8 -1169 2,352 Yes

INT_One+I_Day+M_Num+I_Age 8 -1216 2,448 Yes a

Models including M_Num are not full rank (cannot be evaluated) with either I_Dist or I_Device. 
153

Selected Cumulative Logit Regression Models

The parameter estimates, standard error, odds ratio, and 95-percent confidence interval for the 

odds ratio for the selected Cumulative Logit Regression Models are provided in table 156

through table 158. The models show that as brightness increases, the odds of a participant 

indicating the brightness is less than or equal to a rating of irritating decreases, which means that 

as brightness increases the odds of a participant indicating the discomfort glare is unbearable 

increases. In addition, the models show the participant’s odds of indicating unbearable 

discomfort is about 13 times greater at night than during the day. An atypical finding in this 

study is that the odds of a younger participant indicating unbearable discomfort are about 

1.4 times more likely than older participant’s odds of indicating unbearable discomfort. In the 

next section, researchers use the models in table 157 and table 158 to develop equations 

predicting the probability of a participant indicating a light source has unbearable discomfort. 

Table 156. Cumulative logit model with C_Level as a measure of brightness.

Parameter Estimate

Standard

error

95-percent confidence intervala

Lower bounds Odds ratiob Upper bounds

Intercept (j = 1) 1.5207 0.2655 NA NA NA

Intercept (j = 2) 4.1643 0.2808 NA NA NA

C_Level = 2 -1.7627 0.2897 0.0972 0.1716 0.3027

C_Level = 3 -3.4831 0.2844 0.0176 0.0307 0.0536

C_Level = 4 -4.6302 0.2878 0.0055 0.0098 0.0171

C_Level = 5 -5.3249 0.2938 0.0027 0.0049 0.0087

C_Level = 6 -5.9668 0.3036 0.0014 0.0026 0.0046

I_Day = 1 2.6209 0.1319 10.6161 13.7481 17.8040

M_Num = 2 0.7791 0.1580 1.5991 2.1796 2.9708

M_Num = 3 0.4887 0.1563 1.2000 1.6302 2.2145

M_Num = 4 0.1285 0.1552 0.8389 1.1371 1.5414

I_Age = 1 0.3302 0.1109 1.1194 1.3912 1.7290 a

If the odds ratio confidence interval includes the value 1.000 (which M_Num = 4 does), the difference is not statistically 

significant with 95-percent confidence. b

For C_Level = z, the odds ratio is odds(C_Level = z)/odds(C_Level = 1). For M_Num = z, the odds ratio is 

odds(M_Num = z)/odds(M_Num = 1).

NA = Not Applicable.

Table 157. Cumulative logit model with OP_One as a measure of brightness. 

Parameter Estimate

Standard

error

95-percent confidence intervala

Lower bounds Odds ratiob Upper bounds

Intercept (j = 1) 4.0117 0.2602 NA NA NA

Intercept (j = 2) 6.6042 0.2984 NA NA NA

OP_Onec -0.0001534 0.000006193 NA NA NA

I_Day = 1 2.5577 0.1287 10.0287 16.6091 12.9061

M_Num = 2 0.8730 0.1661 1.7288 3.3153 2.3941

M_Num = 3 -2.1270 0.1875 0.0825 0.1721 0.1192

M_Num = 4 -2.4520 0.1903 0.0593 0.1251 0.0861

I_Age = 1 0.3313 0.1105 1.1216 1.7296 1.3928 a

If the odds ratio confidence interval includes the value 1.000, the difference is not statistically significant with 95-percent 

confidence. 

b

For M_Num = z, the odds ratio is odds(M_Num = z)/odds(M_Num = 1). 

c

It is not appropriate to interpret the parameter estimate of OP_One as an odds ratio because it is a continuous variable. 

NA = Not Applicable.
154

Table 158. Cumulative logit model with INT_One as a measure of brightness. 

Parameter Estimate

Standard

error

95-percent confidence intervala

Lower bounds Odds ratiob Upper bounds

Intercept (j = 1) 5.5347 0.3490 NA NA NA

Intercept (j = 2) 7.9764 0.3819 NA NA NA

INT_Onec -0.003781 0.0001693 NA NA NA

I_Day = 1 2.4113 0.1238 8.7465 11.1484 14.2100

M_Num = 2 0.8889 0.1689 1.7469 2.4325 3.3870

M_Num = 3 -4.7285 0.2840 0.0051 0.0088 0.0154

M_Num = 4 -5.0237 0.2871 0.0037 0.0066 0.0116

I_Age = 1 0.3205 0.1085 1.1139 1.3778 1.7043 a

If the odds ratio confidence interval includes the value 1.000, the difference is not statistically significant with 95-percent 

confidence. 

b

For M_Num = z, the odds ratio is odds(M_Num = z)/odds(M_Num = 1). 

c

It is not appropriate to interpret the parameter estimate of INT_One as an odds ratio because it is a continuous variable. 

NA = Not Applicable.

Prediction Equations

To assist in evaluating the relationship between unbearable discomfort glare, optical power, and 

95th percentile peak intensity, researchers developed prediction equations. These equations 

predict the probability of a participant indicating beacons in this study had unbearable discomfort 

glare. The prediction equations are shown in figure 71 and figure 72.

Figure 71. Equation. Probability of unbearable discomfort glare based on optical power.

Figure 72. Equation. Probability of unbearable discomfort glare based on intensity.

Where:

p(I_ciu = 3)OP = Probability of participant indicating the light level is unbearable with optical 

power as the measure of brightness.

p(I_ciu = 3)INT = Probability of participant indicating the light level is unbearable with 95th

percentile intensity as the measure of brightness.

exp( ) = Exponential function.

OP_one = Optical power of the device measured at a vertical and horizontal angle of zero.

INT_one = 95th percentile intensity of the device measured at a vertical and horizontal angle of 

zero.

I_Day = Indicator variable for daytime; equals 1 if daytime and 0 if nighttime.

I_Age = Indicator variable for age group; equals 1 if 55 years or older, 0 otherwise.

fM_Num(OP) = Factor associated with the measurement number (M_Num); equals 0 if M_Num = 1, 

0.0.8730 if M_Num = 2, -2.1270 if M_Num = 3, and -2.4520 if M_Num = 4.

fM_Num(INT) = Factor associated with the measurement number (M_Num); equals 0 if M_Num = 1, 

0.8889 if M_Num = 2, -4.7285 if M_Num = 3, and -5.0237 if M_Num = 4.

p(I_ciu = 3)OP = 1 / [1 + exp(6.6042 – 0.0001534×OP_one + 2.5577×I_Day + 

0.3313×I_Age + fM_Num(OP))]

p(I_ciu = 3)INT = 1 / [1 + exp(7.9764 – 0.003781×INT_One + 2.4113×I_Day + 

0.3205×I_Age + fM_Num(INT))]
155

Using these prediction equations, researchers produced the curves shown in figure 73 and 

figure 74; these curves show the probability of an older driver (55 years or more) indicating the 

discomfort glare of LED embedded sign is unbearable using the flash pattern from the right 

beacon of an RRFB for daytime and nighttime conditions. In addition to the discomfort glare 

curves, the graphs also indicate the SAE minimum for test point and three times the SAE 

minimum for the test point. Specific values for the optical power and 95th percentile intensity 

when 10 percent, 15 percent, and 50 percent of the older driver population would indicate the 

discomfort is unbearable are provided in table 159; in addition, the percentiles at the SAE 

minimum and three times the SAE minimum are shown in table 160.

Key Findings for Discomfort Glare Study

The data show that for all devices at all distances, the percentage of participants indicating the 

brightness of the lights from the beacons/LEDs is comfortable decreases as brightness increases,

and the percentage of participants indicating the discomfort glare is unbearable increases as 

brightness increases. 

Figure 73. Graph. Older drivers’ probability of unbearable discomfort glare by optical 

power and time of day for LED-embedded signs at 250 ft.
156

Figure 74. Graph. Older drivers’ probability of unbearable discomfort glare by 95th

percentile intensity and time of day for LED-embedded signs at 250 ft.

Table 159. Unbearable discomfort glare for 10th, 15th, and 50th percentiles for LEDembedded signs at 250 ft. 

Brightness

variable I_Day 10thpercentile

15th 

percentile

50th 

percentile

SAE

minimuma

3 times SAE

minimumb

OP_One Day 48,190 51,246 62,703 14,400 43,200

Night 31,296 34,352 45,809 14,400 43,200

INT_One Day 02,251 02,373 02,832 00,600 01,800

Night 01,613 01,736 02,194 00,600 01,800 a

This is the minimum measured at a vertical angle of zero and horizontal angle of zero. b

This is a potential maximum brightness; the Institute of Transportation Engineers specifies a maximum peak luminous intensity 

on traffic signals of three times the minimum intensity value.

Table 160. Unbearable discomfort glare percentiles at SAE minimum and three times the 

SAE minimum for LED-embedded signs at 250 ft. 

Brightness

variable I_Day

SAE

minimuma

Probability

unbearable

(percent)

3 times SAE

minimumb

Probability

unbearable

(percent)

OP_One Day 14,400 0.07 43,200 04.96

Night 14,400 0.85 43,200 40.25

INT_One Day 00,600 0.02 01,800 01.98

Night 00,600 0.24 01,800 18.38 a

This is the minimum measured at a vertical angle of zero and horizontal angle of zero. b

This is a potential maximum brightness; the Institute of Transportation Engineers specifies a maximum peak luminous intensity

on traffic signals of three times the minimum intensity value.

Observations From Discomfort Glare Study

The data indicate agencies should focus on meeting the SAE minimum intensity and place less 

emphasis on obtaining the brightest devices possible. When devices have intensities and optical 
157

powers close to the SAE minima, the probability of unbearable discomfort glare is less than 

1 percent. 

The profession needs maximum brightness for RRFB beacons. As the brightness of the beacons 

on a traffic control device increases, the probability of a driver indicating the discomfort glare is 

unbearable increases. When the discomfort glare is unbearable, drivers are more likely to divert 

their eyes away from the discomfort, which may result in drivers missing hazards located near 

the glare source. 

When setting maximum brightness for RRFB beacons, decision makers should consider a variety 

of distances from the glare source. As the distance from the glare source varies, drivers perceive 

increases and decreases in discomfort glare. A policy on maximum brightness should limit the 

discomfort glare along the length of the approach with a focus on critical distances such a 

decision sight distance, stopping sight distance, and sign legibility distance. 

Setting different nighttime and daytime maximum brightness values might be appropriate. The 

data indicate that under daytime conditions, participants are 13 times more likely to indicate the 

brightness is either comfortable or irritating compared with their responses in nighttime 

conditions (when they were more likely to indicate unbearable instead). This suggests that 

daytime settings could be higher than nighttime settings. 

The discomfort glare results support the concept of dimming traffic control devices during low 

light conditions; however, this study did not investigate the ability of devices below the SAE 

minima to draw a driver’s attention, which means dimmed devices should still meet SAE 

minimum values. 

Brightness specifications for RRFBs should consider alternative measures of brightness, such as 

optical power. Peak intensity is not the only measure of brightness, and in some instances, it may 

not be the best measure of brightness. When quantifying the brightness of traffic control devices, 

researchers should consider alternative measures of brightness in their studies and use statistics 

to determine which measure is the most appropriate.

This study found controller setting (which is highly correlated with intensity and optical power) 

as the measure of brightness that resulted in the model with the best fit of data. This indicates 

participants may have been responding to the setting of the controller at measurement locations 

two, three, and four rather than the brightness of the traffic control device. 

159

CHAPTER 6. OPEN-ROAD STUDY

STUDY DEVELOPMENT

Study Sites

Toward the conclusion of the closed-course study, the researchers talked with city representatives 

and made requests during professional society meetings, seeking cities that would be willing to 

participate in the open-road research. Four cities volunteered: Milwaukee, WI; Flagstaff, AZ; 

Austin, TX; and College Station, TX. As a minimum, the cities were asked to identify at least two 

locations that allowed the treatments to be installed in one location and then rotated to the other 

location after the initial data collection. Table 161 lists the sites included in the study. 

Table 161. Study site characteristics.

Site

Posted 

speed 

limit 

(mi/h)

Total 

crossing 

distance (ft)

Crossing 

distance to 

refuge (ft)

Calculated

daily traffic 

(vehicles/day)

Advance 

yield lines?

Number 

of lanes

Presence 

of median

AU-01 35 44 44 17,732 No 4 None

AU-02 30 56 22 9,096 No 3 Raised

CS-01 30 48 48 2,130 No 2 None

CS-02 40 60 60 16,496 Yes 4 TWLTL

FG-01 35 84 31.5 23,008 No 4 Raised

FG-02 30 55 21 19,297 No 2 Raised

FG-03 35 76 28.5 14,590 No 4 Raised

MK-04 30 39 39 7,238 No 2 None

MK-05 30 40 40 6,883 No 4 None

MK-06 30 80 31 13,312 No 4 Raised

MK-07 30 98 42 11,401 No 6 Raised

MK-08 30 49 49 10,117 No 4 None

The calculated daily traffic (CDT) was determined based on 1-hour counts made from the video 

recordings. The values for hourly percentage of ADT were determined using hourly traffic 

distributions available in the 2011 Urban Mobility Report.

(34) The distributions for non-freeway, 

a.m. and p.m. peak periods for both no/low congestion and moderate congestion were considered 

to obtain typical weekday values. These volume distributions were combined to generate an 

overall representative distribution for use in this project. Figure 75 shows the hourly distribution 

used to adjust the 1-h counts into CDT values. For example, the 1-h count at MK-05 was 

382 vehicles and was done from 11 a.m. to noon. That hour represents 5.55 percent of the daily 

traffic, so 382/0.0555 = 6,883 vehicles/day.
160

Figure 75. Graph. Typical hourly distribution used to convert 1-h volume into CDT.

Study Assemblies

Examples of the study assemblies are shown in figure 76 through figure 87. The beacons were 

mounted on a roadside pole to supplement a W11-2 (pedestrian) crossing warning sign with a 

diagonal downward arrow (W16-7P) plaque, and located at or immediately adjacent to a marked 

crosswalk. The RRFBs were mounted and operated as detailed in the FHWA Interim Approval 

and/or the subsequent official interpretations issued by FHWA on RRFB installations. The 

circular beacons used high-intensity LED-based indications (meeting Class 1 requirements), and 

used the rapid-flashing pattern used by the RRFB. The 12-inch circular beacons were mounted 

beneath the pedestrian crossing sign. 

The flash pattern used at the sites was the 2-5 pattern currently used with the RRFBs. In the 

2-5 pattern, one side of the beacon pulses twice followed by the other side pulsing five times.

Table 162 provides information on installation order along with the dates of the data collection.

Rotation

To account for the potential that device installation order could affect the results, the RRFB was 

installed first in some locations and second in other locations. For the 12 study sites, the RRFB 

was installed first in half of the sites while the CRFB was installed first in the other half of the 

sites.

Brightness of Beacons 

Because preliminary findings from another closed-course study indicated that brightness of the 

beacons can influence how quickly a participant can detect a pedestrian within a crosswalk, 

efforts were made to measure the brightness of the beacons included in this study.(34) Because of

limited funds, only one trip was made to each city to measure brightness. When knowledge was 
161

available regarding the new location of a measured beacon after rotation, the brightness level of 

that beacon in the new location was assumed to be the same as was measured in the previous 

location. 

Figure 76. Photo. Rectangular beacons used at CS-01.

Figure 77. Photo. Circular beacons used at CS-02.

Figure 78. Photo. Circular beacons used at AU-01.

Figure 79. Photo. Rectangular beacons used at AU-02.
162

Figure 80. Photo. Circular beacons used at MK-04.

Figure 81. Photo. Circular beacons used at MK-05.

Figure 82. Photo. Circular beacons used at MK-06.

Figure 83. Photo. Rectangular beacons used at MK-07.
163

Figure 84. Photo. Rectangular beacons used at MK-08.

Figure 85. Photo. Circular beacons used at FG-01.

Figure 86. Photo. Circular beacons used at FG-02.

Figure 87. Photo. Rectangular beacons used at FG-03.
164

Table 162. Installation dates and dates of data collection.

Site

Existing 

beacon on 

assemblya

Date of 

before data 

collection

Initial 

assembly

Date CRFB 

installed

Date of 

CRFB data 

collection

Date RRFB 

installed

Date of 

RRFB data 

collection

AU-01 24/7 8/29/2013 CRFB 2/3/2014 3/26/2014 4/10/2014 4/24/2014

AU-02 Activated 8/28/2013 RRFB 4/10/2014 4/23/2014 3/7/2014 3/25/2014

CS-01 24/7 11/15/2012 RRFB 3/6/2014 4/2/2014 11/27/2014 2/14/2014

CS-02 None 1/28/2013 CRFB 11/27/2014 2/5/2014 3/6/2014 4/1/2014

FG-01 Activated 5/21/2013 RRFB 1/9/2014 3/5/2014 9/2/2013 10/23/2013

FG-02 RRFB NAb RRFB 1/9/2014 3/5/2014 7/11/2013 10/22/2013

FG-03 None 5/21/2013

5/22/2013 CRFB 9/7/2013 10/23/2013 12/4/2013 3/5/20143/6/2014

MK-04 None 6/18/2013 CRFB 8/1/2013 9/24/2013 10/21/2013 12/5/2013

MK-05 None 6/19/2013 CRFB 8/1/2013 9/23/2013 10/21/2013 12/5/2013

MK-06 None NAc CRFB 8/5/2013 9/23/2013 10/21/2013 12/5/2013

MK-07 RRFB NAb RRFB 10/21/2013 12/6/2013 Existing 

condition 6/19/2013

MK-08 RRFB NAb RRFB 10/21/2013 12/4/2013 Existing 

condition 6/20/2013

a

24/7 = continuously active yellow flashing beacons; Activated = pedestrian-activated yellow flashing beacons. b

No before data collected because the RRFBs were already installed at the site. c

Road work was being conducted near the site during the data collection trip; therefore, no before data were collected.

NA = Not Available.

DATA COLLECTION

Study Periods

The study was conducted between November 2012 and April 2014. The before data were 

collected between November 2012 and August 2013. Once the before data were obtained, the 

research team requested the city to install the initial device—at approximately half the sites, the 

initial device was the RRFB; at the other half of the sites, it was the CRFB. Following 

installation of the initial device (and a minimum period of 1 month for drivers to get acclimated 

to the new device), the research team collected “after data.” Once the after data were obtained, 

the research team requested the city to install the second device (thus, RRFBs were replaced with 

CRFBs and vice versa). Once again, following a minimum period of 1 month for drivers to get 

acclimated to the second device, the research team collected after data for the second device. The 

CRFB and RRFB data were collected between June 2013 and April 2014. 

Data were collected primarily during the daytime when vehicles were free-flowing. Because few, 

if any, studies have collected data at night, the research team wanted to obtain some data for 

nighttime conditions. The characteristics of the beacon and the site might have different impacts 

on driver yielding during night conditions compared with daytime conditions. Therefore, 

nighttime data were collected for one of the sites in each city.
165

Staged Pedestrian Protocol

The research team used a staged pedestrian protocol to collect driver yielding data to ensure that 

oncoming drivers receive 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 a recent paper.(12) For this study, data for a 

minimum of 60 staged pedestrian crossings were collected at each site in each time period during 

daytime. Because of the length of time needed to collect the crossing data, data for a minimum of 

40 staged pedestrians were collected at night with one exception. Because of the very low 

yielding and the frequency of the staged pedestrian being stranded in the TWLTL, data 

collection was stopped at one of the sites.

Video

The team also video-recorded the sites for approximately 4 to 6 hours for each study period. The 

video data provided the opportunity to confirm characteristics of a crossing, including whether 

the pedestrian pushed the pushbutton to activate the beacons. Volume counts were reduced for a 

sample of the video recorded at the RRFB sites.

Brightness of Beacons 

Background

Formal definitions of luminance intensity, brightness, optical power, and other photometric 

measurement terms are listed in table 163. While the term optical power is more traditionally 

associated with optical convergence or divergence of a lens, in this memo, the SAE definition is 

used.(35)

Equipment

The equipment responsible for the light measurements includes a photometer with a cosinecorrected photometric sensor and an oscilloscope with high-speed photodetector. The cosinecorrected photometric sensor is rated to capture light with wavelengths between 400 and 

700 nanometers, which is also the spectrum of visual light. The high-speed photodetector is rated 

for capturing light with wavelengths between 400 and 1,100 nanometers, which includes infrared 

light in addition to the visual spectrum of light.(36) All of the equipment used and purpose of the 

equipment are listed in table 164. 
166

Table 163. Photometric terminology. 

Term Definition

Brightness “Attribute of a visual perception according to which an area appears to emit, or 

reflect, more or less light.”(37)

Illuminate energy 

(lux-seconds) Quantity of light falling on a sensor from a light source. 

Luminous Intensity (candela) “The luminous flux per unit solid angle in a given direction expressed in candela 

(cd).”(38) (p. 1)

Luminous energy

(candela-seconds) Luminous flux per unit solid angle in a given direction for a duration of time. 

Optical Power 

(candela-seconds/minute)

“The integration of the luminous intensity of the flashing light source for a time of 

60 seconds.”(35) (p. 3)

Light Pulse “A single, visually continuous emission of optical energy. High frequency 

modulation is permitted.”(35) (p. 3)

Flash

“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.”(35) (p. 3)

Table 164. Equipment list and purpose.

Equipment Purpose

Photometer with cosine-corrected 

photometric sensor(39) Illuminating energy measurement (lux-seconds)

Oscilloscope with high-speed 

photodetector

Capturing flash pattern (time)

Determining relative beacon intensity

Tripod and mounting equipment Positioning the photometric sensor and photodetector in front of the 

beacons

Laptop computer Recording the illuminate energy and capturing the flash pattern

Portable generator Powering the photometer and oscilloscope

Electric measuring tape Measuring the distance between the cosine-corrected photometric sensor 

and the light source

Brightness Data Collection

Researchers collected illuminate energy and flash pattern digital images of beacons in cities 

participating in the CRFB/RRFB open-road study. The dates of the data collection by site are 

listed in table 165. All of the data were collected at night because ambient light from the sun 

would overwhelm the sensors if researchers used the same methods during the day. 

Table 165. Data collection dates by site and city. 

Site ID Beacon shape City Night(s)

AU-01 Circular Austin March 6 to 7, 2014

AU-02 Rectangular Austin March 6 to 7, 2014

CS-01 Rectangular College Station January 21, 2014

CS-02 Circular College Station January 21, 2014

FL-01 Circular Flagstaff March 3 to 5, 2014

FL-02 Circular Flagstaff March 3 to 5, 2014

FL-03 Rectangular Flagstaff March 3 to 5, 2014

MI-04 Circular Milwaukee September 22 to 24, 2013

MI-05 Circular Milwaukee September 22 to 24, 2013

MI-06 Circular Milwaukee September 22 to 24, 2013

MI-07 Rectangular Milwaukee September 22 to 24, 2013

MI-08 Rectangular Milwaukee September 22 to 24, 2013
167

Upon arriving at the data collection location, researchers would mount the photometric sensor 

and high-speed photodetector to the tripod. Then, to the best of their ability, the researchers 

positioned the cosine-corrected photometric sensor in front of the yellow warning beacons such 

that measurements were taken at a horizontal (left/right) angle of zero degrees and a vertical 

(up/down) angle of zero degrees. In addition, researchers positioned the photometric sensor 

between 3 and 12 ft away from the beacons. In this study, the beacons were either 12-inch 

circular beacons mounted below the sign or rectangular beacons mounted below the sign. 

After positioning the sensors, the research team confirmed the equipment was working properly 

before taking measurements. The researcher responsible for taking measurements took five 

measurements of background illuminate energy. During data reduction, researchers used these 

measurements to quantify and remove background illuminate energy from the calculated values 

of luminance energy and luminance intensity for the beacons. Background illuminate energy 

often came from streetlights. The duration of the illuminate energy readings was between 1 and 

2 s depending on the distance from the light source and volume of illuminate energy. (The 

photometer was unable to record illuminate energy values greater than 500 lx-s.) 

After taking the background illuminate energy readings, the research team activated the beacons 

and took digital images of the flash pattern using the high-speed photodetector and digital 

oscilloscope. Each digital image consisted of 4 s worth of data recorded as 4,000 individual 

energy readings. (The high-speed photodetector and oscilloscope documented the flash pattern in 

terms of voltage.) Each digital image was saved to the computer in a spreadsheet. 

Once the digital images of the flash patterns were recorded, the researcher responsible for taking 

measurements took five illuminate energy readings while the beacons were activated (flashing). 

The duration for each reading was the same as the duration of the background readings. 

Before moving the sensors to the next set of beacons at the site, the research team recorded the 

distance from the cosine-corrected photometric sensor to the light source in meters. During data 

reduction, researcher used this value to convert illuminate energy (lx-s) to luminance energy 

(cd-s). In addition, the research team documented which beacon (left or right) in the set had two

pulses and which beacon had more than two pulses. 

After confirming all of the necessary data were collected, the research team then moved to the 

next set of beacons at the site. It took the research team between 90 and 180 min to collect all of 

the illuminate energy readings and flash pattern images required for sites with four sets of 

beacons to measure. 

DATA REDUCTION 

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 88; however, data for individual crossings were 

used in the statistical evaluation.
168

Figure 88. Equation. Yielding rate for a single crossing and average yielding rate for a site.

Brightness

Determine Luminance Energy and Optical Power for Each Set of Beacons

The first step in determining the luminance intensity and luminance energy for each set of 

beacons and individual lamps was to average the illuminate energy background measurements 

and illuminate energy readings when the beacons were active. Researchers then subtracted the 

average background illuminate energy from the average illuminate energy with activated 

beacons to determine the average beacon illuminate energy. Average beacon illuminate energy is 

then converted to luminance energy using the equation in figure 89.

Figure 89. Equation. Average luminance energy.

Where:

lum_energy = average beacon luminance energy (cd-s). 

ill_energy = average beacon illuminate energy (lx-s). 

m_dist = distance between the cosine-corrected photometric sensor and the beacons (m). 

After determining the average luminance energy, researchers converted luminance energy to 

optical power, which is luminance energy per minute. For this memo, optical power is calculated 

using the equation in figure 90:

Figure 90. Equation. Optical power.

Where:

lum_energy = average beacon luminance energy (cd-s). 

m_dur = duration of the illuminate energy reading used to calculate luminance energy (s). 

OP_both = optical power of the beacon set (cd-s/min). 

Determine on Time and Cycle Length for Each Beacon Set

After determining the optical power for each set of beacons, the next step in the process of 

determining the luminous intensity for individual beacons is to determine the total on time, 

individual lamp on time, and cycle length for each beacon. To determine these values, 

researchers used the digital images of the flash patterns captured using the oscilloscope and highspeed photodetector. 

The first step in the process of determining on time and cycle length is to convert the voltage 

values in the digital images to percent maximum voltage; then, to determine which values count 

as on and which values count as off, researchers used a trigger level of 10 percent of the 

maximum voltage. This means that if the percent maximum voltage was under 10 percent, then 

YYYYYYYYYYYYYYYY rrrrrr = nnnnnn oooo yy vv h ii ii

nnnnnn oooo yy vv h ii ii + nnnnnn oooo ̵yy vv h ii ii

lum_energy = ill_energy × m_dist2

OP_both = lum_energy * 60 / m_dur
169

the lamp was considered to be off and that time was assigned a percent maximum voltage of 

zero. Sometimes, owing to background illuminate energy, researchers adjusted up to 20 percent 

of the maximum voltage, but this rarely occurred. 

Next, researchers used a computer macro to sort the data into four separate flash cycles of about 

800 ms. A researcher then graphed each flash cycle and reviewed these graphs to make sure the 

image was captured correctly before performing calculations. An example of a graphed flash 

cycle that was captured correctly is shown in figure 91. 

Each data point in figure 91 represents 1 ms of data. Therefore, to determine cycle length, 

researchers counted the number of data points in each correctly captured flash cycle and 

averaged these values to determine the cycle length in milliseconds. To determine on time, 

researchers counted the number of data points that were greater than zero and averaged these 

values to determine the on time in milliseconds. Owing to background lighting, some points that 

were greater than zero would be filtered to zero during the data analysis. This ensured that 

readings that were greater than zero based on the background light and not the beacon being 

active were accounted for properly. 

After determining the total on time and cycle length, researchers then determined the on time for 

the beacon with five flashes (the first five flashes in figure 91) and the on time for the beacon 

with two flashes (the sixth and seventh flashes in figure 91). 

Figure 91. Graph. Example of correctly captured and graphed flash cycle showing five 

pulses of light (0.0 to 0.4 time) followed by two pulses of light (0.4 to 0.8 time). 

0

20

40

60

80

100

0.00 0.20 0.40 0.60 0.80 1.00

Percent Maximum Intensity (%)

Time (seconds)
170

FINDINGS

Driver Yielding Findings

Table 166 shows the driver yielding results for each site and assembly along with the number of 

nonstaged and staged pedestrian crossings for the daytime data collection periods. During the 

daytime, driver yielding to staged pedestrians averaged 63 percent for the CRFB and 59 percent 

for the RRFB assemblies. The range of driver yielding to staged pedestrians at yellow, rapidflashing beacons ranged from a low of 22 percent to a high of 98 percent. 

Table 167 lists the driver yielding rates for the nighttime data collection periods. Driver yielding 

to staged pedestrians at night averaged 72 percent for the RRFB assemblies and 49 percent for 

the CRFB assemblies; however, the 49 percent figure includes data for a site that had a beacon 

that did not meet the Class 1 requirement for brightness. Without that site, the average yielding 

for the CRFB was 69 percent.

Table 166 and table 167 also contain the driver yielding rates in the before condition (i.e., before 

the installation of the rapid-flashing beacon) for those sites where data were available. An 

evaluation of whether a statistically significant increase was present between the before 

conditions (slow flashing beacon or no flashing treatment) and the rapid-flashing beacon 

condition could not be completed because of the limited number of sites with a before treatment.
171

Table 166. Daytime driver yielding rate by site and assembly.

Assembly Site

Nonstaged 

count

Nonstaged driver 

yielding Staged count

Staged driver 

yielding

CRFB

AU-01 28 90% 60 62%

AU-02 13 54% 60 57%

CS-01 41 97% 39 89%

CS-02 11 48% 60 36%

FG-01 131 83% 81 94%

FG-02 73 93% 69 95%

FG-03 37 90% 66 95%

MK-04 109 12% 62 58%

MK-05 104 13% 78 44%

MK-06 130 22% 56 67%

MK-07 62 58% 62 39%

MK-08 30 30% 60 58%

All CRFB 769 46% 753 63%

All CRFB (without site CS-02) 717 44% 654 67%

RRFB

AU-01 26 71% 60 71%

AU-02 82 43% 60 59%

CS-01 58 97% 60 56%

CS-02 17 49% 60 44%

FG-01 95 77% 69 84%

FG-02 77 92% 61 98%

FG-03 25 98% 80 98%

MK-04 79 25% 61 43%

MK-05 40 24% 59 22%

MK-06 36 23% 61 57%

MK-07 114 48% 61 31%

MK-08 128 37% 82 36%

All RRFB 777 57% 774 59%

Slow

AU-01 83 22 60 12

AU-02 33 26 61 51

CS-01 77 100 42 66

FG-01 89 79 100 85

All Slow Flashing 282 58 263 54

None

CS-02 10 10 40 9

FG-03 37 87 80 62

MK-04 121 18 96 19

MK-05 88 12 89 9

All No Flashing 256 26 305 24
172

Table 167. Nighttime driver yielding rate by site and assembly.

Assembly Site

Nonstaged 

count

Nonstaged driver 

yielding Staged count

Staged driver 

yielding

CRFB

AU-02 0 NA 40 67%

CS-02 0 NA 19a 5%a

FG-01 23 85% 52 89%

MK-08 6 56% 60 52%

All CRFB (without site CS-02) 29 78% 152 69%

RRFB

AU-02 0 NA 40 88%

CS-02 0 NA 40 53%

FG-01 21 100% 50 95%

MK-08 9 20% 50 54%

All RRFB 30 84% 180 72%

Slow FG-01 7 100% 58 63% a

Data should not be considered because beacon brightness did not satisfy the Class 1 requirement.

NA = Not Applicable (no nonstaged pedestrians were observed at night at these sites).

Brightness Findings

Determine Average Intensity

After determining the cycle length, total on time, on time for the five-flash beacon, and on time 

for the two-flash beacon, it is possible to determine the average luminance intensity for each 

beacon set and individual beacons. The equation for determining the average intensity for each 

beacon set is shown in figure 92:

Figure 92. Equation. Average intensity.

Where:

int_both = the average luminance intensity for both beacons (cd).

cycle = cycle length for the beacon set (ms). 

on_both = total on time for both beacons (ms). 

OP_both = optical power of the beacon set (cd-s/min). 

Determine Optical Power and Average Luminance Intensity by Approach 

The optical power by approach can be calculated using the equation in figure 93.

Figure 93. Equation. Optical power by approach.

Where:

op_approach = optical power for a vehicle traveling in a given direction (cd-s/min).

op_left = optical power for a set of beacons on the left side of the roadway (cd-s/min).

op_right = optical power for a set of beacon on the right side of the roadway (cd-s/min). 

The average intensity by approach can be calculated using the equation in figure 94.

int_both = OP_both × cycle / [ 60 × on_both ]

op_approach = op_left + op_right
173

Figure 94. Equation. Average intensity by approach.

Where:

int_approach = average intensity for an approach (cd)

int_left = intensity for a set of beacons on the left side of the roadway (cd). 

on_left = on time for the set of beacons on the left side of the roadway (s). 

int_right = intensity for a set of beacons on the right side of the roadway (cd).

on_right = on time for the set of beacons on the right side of the roadway (s). 

Determine Optical Power and Average Luminance Intensity by Site

Because motorist yielding calculations are done for a site rather by an approach, a value is 

needed to reflect the site. The average optical power for a site would be the average of the two 

directions or approaches. The optical power for an approach is the sum of the optical power for 

the assembly on the left and the assembly on the right because a driver would have both 

assemblies in view. For a site, the driver would only have the assemblies on approach A or on 

approach B in view; therefore, an average the optical power for the two approaches is needed. It 

can be calculated using the equation in figure 95.

Figure 95. Equation. Optical power for a site.

Where:

op_site = average optical power for site (cd-s/min).

op_approach-A = optical power for a vehicle traveling in a given direction (cd-s/min).

op_approach-B = optical power for a vehicle traveling in the opposing direction (cd-s/min). 

To provide an appreciation of how long a beacon is on, the average intensity for a site should be 

a weighted average. It can be calculated using the equation in figure 96:

Figure 96. Equation. Average intensity for a site.

Where:

int_site = average intensity for a site (cd).

int_left_A = intensity for left-side beacons on approach A (cd).

int_right_A = intensity for right-side beacons on approach A (cd).

int_left_B = intensity for left-side beacons on approach B (cd).

int_right_B = intensity for right-side beacons on approach B (cd).

on_left_A = on time for left-side beacons on approach A (s).

on_right_A = on time for right-side beacons on approach A (s).

on_left_B = on time for left-side beacons on approach B (s).

on_right_B = on time for right-side beacons on approach B (s).

int_approach = (int_left × on_left + int_right × on_right) / (on_left + on_right)

op_site= (op_approach-A + op_approach-B)/2

int_site = (int_left_A × on_left_A + int_right_A × on_right_A + int_left_B × on_left_B + 

int_right_B × on_right_B ) / (on_left_A + on_right_A + on_left_B + on_right_B)
174

The measured average optical power and average intensity by site are provided in table 168 and

table 169, respectively. Figure 97 shows a plot of the optical power values while figure 98 shows 

average intensity per site. In most cases, the research team was able to track where the beacons 

were moved after a rotation. 

Table 168. Average optical power and intensity by crossing. 

Shape Site

Average intensity (cd), rounded 

to hundreds

Average optical power (cd-s/min), rounded 

to thousands

CRFB AU-01 500 37,000

RRFB AU-02 3,000 226,000

RRFB CS-01 1,400 117,000

CRFB CS-02 100 8,000

CRFB FG-01 1,800 156,000

CRFB FG-02 1,100 80,000

RRFB FG-03 1,200 93,000

CRFB MK-04 1,100 87,000

CRFB MK-05 2,100 162,000

CRFB MK-06 1,900 150,000

RRFB MK-07 1,100 84,000

RRFB MK-08 800 66,000

Table 169. Device brightness at each site.

Site

CRFB-intensity 

(cd)

CRFB-optical power 

(cd-s/min)

RRFB-intensity 

(cd)

RRFB-optical power 

(cd-s/min)

AU-01 600 37,000 3,100 220,000

AU-02 600 37,000 3,100 220,000

CS-01 100 8,000 1,500 117,000

CS-02 100 8,000 1,500 117,000

FG-01 1,900 165,000 NA NA

FG-02 1,200 83,000 NA NA

FG-03 NA NA 1,200 95,000

MK-04 1,100 87,000 1,200 91,000

MK-05 2,100 162,000 1,000 77,000

MK-06 1,900 150,000 800 66,000

MK-07 1,600 124,000 1,100 84,000

MK-08 1,900 150,000 800 66,000

NA = Not Available.
175

Figure 97. Graph. Plot of average optical power by beacon shape and site.

Figure 98. Graph. Plot of average intensity by beacon shape and site.
176

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 in this study, that means more than 2,500 data points could 

be available (i.e., all the unique staged crossings recorded) rather than only 41 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 CRFB to the RRFB, that means 1,878 data points rather than 32 data 

points are available. When examining both staged and nonstaged data, there are 3,483 crossings 

available. These larger sample sizes could result in finding significant relationships that would 

not be apparent with a smaller dataset. In addition, it is possible to use random effects to account 

for site-specific differences because 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 based on the yield rate changes directly, but a change in the odds of motorists 

yielding. (The 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 statistical literature.(40) All the statistical analyses were performed using R, an 

open-source statistical language and environment and two open-source packages for fitting 

GLMMs.

(41,42,27)

Comparison of CRFB to RRFB

Because a previous study that included RRFBs found that posted speed limit, crossing distance, 

and city influenced driver yielding, the initial analyses were also conducted with those 

variables.(12) In addition, a measure of the rotation was also recorded, to potentially account for a 

learning curve in the driver yielding rates, as was indicated in the TxDOT study.

(12) Therefore, 

the variable “order” was added as a nested random effect in each site. 

Preliminary modeling revealed a correlation between crossing distance and speed limit present in 

the dataset; therefore, only crossing distance was included in the final model. Attempts were 

made to conduct the analysis separately for nighttime and daytime conditions; however, 

insufficient data were available for the nighttime analysis. To determine whether nighttime 

results were significantly different from daytime results, an indicator variable for nighttime 

conditions was included in the final model. The model specification was a quasi-binomial 

regression because the dataset exhibited significantly more dispersion than it would be expected 

for binomially distributed data. This type of model adjusts maximum likelihood standard errors 

using an estimate of overdispersion to reduce risk of type-I error in the analysis.
177

From the preliminary review of the results in table 166, it appears that there are only minor, if 

any, differences between the CRFB and the RRFB. The results from the GLMM are shown in

table 170, and these results support that observation. These results indicate that there are no 

significant differences between the two beacon shapes (p-value = 0.4792). The day/night variable 

was not significant (p-value = 0.5152), which indicates that there is not enough nighttime data to 

differentiate from daytime rates as a flat effect, after accounting for the rest of the variables in 

the analysis. Preliminarily, it appears that the city of Flagstaff has notably higher driver yielding 

compared with the base city of Austin (an adjusted p-value for multiple comparisons is required 

to make a formal assessment) while College Station and Milwaukee are clearly not different 

from Austin. The model also showed that calculated daily traffic was not a significant variable

(p-value = 0.3157). The results also revealed that the correlation in the data structure and order of 

installation are rather weak effects, judging by the intraclass correlation coefficient, which is a 

measure of how similar data points are within groups when aggregated by site and by order of 

installation.

Table 170. GLMM results comparing CRFB to RRFB.

Variablea,b Estimate Standard error DF t-value p-value

Reference Levelc 1.94372 0.706795 1,837 2.750049 0.006

City: College Station -0.24829 0.700020 6 -0.354687 0.735d

City: Flagstaff 2.37791 0.512182 6 4.642705 0.0035d

City: Milwaukee -0.80018 0.439072 6 -1.822435 0.1182d

CrossDis -0.02137 0.021964 6 -0.973127 0.3681

Treatment: RRFB -0.15289 0.205130 11 -0.745352 0.4717

Day.Night: Night 0.07484 0.113837 1,837 0.657453 0.511

Calculated Daily Traffic -0.00004 0.000037 6 -1.094477 0.3157 a

Column headings are defined as follows:

• Variable: Fixed Effects variables included in model.

• 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.

• Standard Error = Standard error of value.

• DF = degrees of freedom.

• t-value = conservative estimate of the z-value, which is the standard normal score for estimate, given the hypothesis 

that the actual odds ratio equals one.

• p-value: Probability that the observed log-odds ratio be at least as extreme as estimate, given the hypothesis that the 

actual odds ratio equals one. b

Random effects variables: Site (intraclass correlation coefficient = +0.045) and Site X Order (intraclass correlation coefficient = 

+0.169). c

Reference Level Yielding in the model is estimated at 78.02 percent for the following conditions: 

• City = Austin.

• Day.Night = Day.

• Treatment = CRFB. d

These p-values require an adjustment for multiple comparisons if inferences about different yielding rates among cities are 

intended.

Comparison of CRFB to RRFB Considering Beacon Brightness

For a subset of the sites, the brightness of the beacons was measured. The results for the analysis 

that included intensity as a measure of brightness are shown in table 171. Again, the findings 

illustrate, preliminarily, that driver yielding in Flagstaff is higher than in Austin. This analysis 

included a separate account of intensity under those conditions because the effects of beacons 

might differ between daytime and nighttime. 
178

Figure 99 for daytime and figure 100 for nighttime show graphs of average driver yielding per 

site versus intensity that illustrate a trend of increasing driver yielding associated with increases 

in intensity. Furthermore, the statistical analysis results also demonstrated an increasing yielding 

rate with increasing intensity; however, only at night (p-value < 0.0001 from the quasi-binomial 

regression model, see Intensity:Night in table 171). Still, the trend is in the same direction during 

the day but with a smaller magnitude that the analysis found statistically insignificant. It is 

estimated that the odds of yielding at night increase by a multiplicative factor of 1.0008 per 

additional candela of intensity. 

Including brightness in the analysis does not modify the findings for treatment. Without 

brightness, the results (as shown in table 170) are that there was no difference between the 

circular and rectangular shaped beacons. When considering brightness, the circular beacons still 

do not have significantly different yielding rates compared with rectangular beacons (as shown 

in table 171). Table 172 and table 173 show the estimated daytime and nighttime effects on a 

theoretical site for the range of intensity in this study (100 to 3,100 cd). For reference, column 5 

of these tables shows the raw data averages. The sixth column shows how many crossings are 

available to compute the raw data estimates. The results demonstrate the strong effect for 

intensity at night that is also evident in the raw data averages. 

Table 171. GLMM results comparing CRFB to RRFB when beacon brightness data are 

available.

Variablea,b Estimate Standard

error

DF t-value p-value

Reference Levelc 1.2670 0.7413 1,593 1.7091 0.0876

City: College Station 0.2310 0.6303 7 0.3669 0.7245d

City: Flagstaff 2.2340 0.4830 7 4.6261 0.0024d

City: Milwaukee -0.5060 0.4006 7 -1.2620 0.2474d

CrossDis -0.0260 0.0197 7 -1.3330 0.2243

Treatment: RRFB -0.2430 0.1567 7 -1.5528 0.1644

Intensity 0.0002 0.0001 7 1.5855 0.1569

Day.Night: Night -0.8910 0.2320 1,593 -3.8412 0.0001

Intensity X Day.Night 0.0007 0.0002 1,593 4.4355 < 0.0001 a

Column headings are defined as follows:

• Variable: Fixed Effects variables included in model.

• 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.

• Standard Error = Standard error of value.

• DF = degrees of freedom.

• t-value = conservative estimate of the z-value, which is the standard normal score for estimate, given the hypothesis 

that the actual odds ratio equals one.

• p-value: Probability that the observed log-odds ratio be at least as extreme as estimate, given the hypothesis that the 

actual odds ratio equals one. b

Random effects variables: Site (intraclass correlation coefficient = +0.093) and Site X Order (intraclass correlation coefficient = 

+0.138). c

Reference Level Yielding in the model is estimated at 78.02 percent for the following conditions: 

• City = Austin.

• Day.Night = Day.

• Treatment = CRFB. d

These p-values require an adjustment for multiple comparisons if inferences about different yielding rates among cities are 

intended.
179

Figure 99. Graph. Driver yielding compared with beacon brightness intensity for day.

Figure 100. Graph. Driver yielding compared with beacon brightness intensity for night.

Table 172. Daytime effect of intensity on driver yielding for a theoretical site and for the 

raw data averages.

Intensity (cd) Odds ratio

Initial YR = 

50 percent

(percent)

Initial YR =

60 percent

(percent)

Initial YR =

70 percent

(percent)

Raw data 

average

(percent)

Number of 

crossings

100 1.02 50.0 60.0 70.0 58.8 91

800 1.14 53.4 63.2 72.8 54.8 143

1,900 1.38 58.0 67.4 76.3 78.6 197

3,100 1.69 62.8 71.7 79.8 71.2 120

YR = Yielding Rate.
180

Table 173. Nighttime effect of intensity on driver yielding for a theoretical site and for the 

raw data averages.

Intensity (cd) Odds Ratio

Initial YR =

50 percent 

(percent)

Initial YR =

60 percent 

(percent)

Initial YR =

70 percent 

(percent)

Raw data 

average

Number of 

crossings

100 1.09 50.0 60.0 70.0 6.3 19

800 1.97 66.3 74.7 82.1 66.7 50

1,900 4.98 83.3 88.2 92.1 78.3 112

3,100 13.73 93.2 95.4 97.0 92.9 40

YR = Yielding Rate.

When considered together, analysis of results from table 172 and table 173 indicate that the 

difference in yielding rate associated with the day or night varies depending on the beacon 

intensity level, as shown in table 174. This table shows that nighttime driver yielding was 

significantly lower than daytime when the intensity was 800 cd or less. If a 0.10 significance 

level is used, daytime yielding is significantly higher than nighttime for intensities larger than 

1,800 cd.

Table 174. Multiple comparisons for natural light effect on driver yielding by intensity.

Odds ratio (OR)

compared to 1.0 OR Ln(OR)

Standard 

error z-value p-value Significanta

Night/Day (0 cd) 0.41 -0.89142 0.23142 -3.852 <0.001 ***

Night/Day ( 800 cd) 0.70 -0.35079 0.14502 -2.419 0.0401 *

Night/Day (1,400 cd) 1.06 0.05468 0.12660 0.432 0.8983 —

Night/Day (1,800 cd) 1.38 0.32499 0.14807 2.195 0.0698 —

a

Significance values are as follows: — = p-value > 0.05, * = p-value ≤ 0.05, ** = p-value < 0.01 *** = p-value < 0.001.

Comparison of Driver Yielding When Beacon Activated to Beacon Not Activated

The purpose of a rapid-flashing beacon—whether it is circular or rectangular—is to draw 

drivers’ attention to a pedestrian waiting to cross the roadway and, thus, encourage drivers to 

yield to that pedestrian. Therefore, a key question to be answered in the research was to what 

extent did the presence of an actively flashing beacon influence driver yielding. To address this 

question, driver yielding rates were compared between the pedestrian crossings when a beacon 

was activated and the pedestrian crossings when a beacon was not activated.

The analysis included RRFBs and CRFBs and focused on crossings during daytime study 

periods. An initial comparison of staged pedestrian crossings (Note: staged pedestrians activated 

the beacon every time they crossed the roadway) and those nonstaged pedestrian crossings for 

which the beacon was activated showed no statistically significant difference in driver yielding. 

Therefore, the analysis included both staged and nonstaged pedestrian crossings.

The data consisted of a total of 1,970 activated and 476 non-activated crossings, for a total of 

2,446 crossings used in the analysis. The distribution of activated and non-activated crossings in

each city and site is shown in table 175. 
181

Table 175. Number of activated and non-activated crossings by city and site. 

City Site

Beacon activation

Yes No Total

Austin AU-01 149 9 158

AU-02 134 44 178

College Station CS-01 126 5 131

CS-02 136 4 140

Flagstaff

FG-01 266 71 337

FG-02 255 16 271

FG-03 192 4 196

Milwaukee

MK-04 130 95 225

MK-05 147 39 186

MK-06 132 98 230

MK-07 147 46 193

MK-08 156 45 201

Total 1,970 476 2,446

A GLMM with a binomial distribution and logit link was used to model the probability of 

yielding as a function of the following variables:

• Beacon activation (Yes/No).

• City (4 levels).

• Interaction between beacon activation and city (8 levels).

• CDT (continuous; ranging from approximately 2,130 to 23,008 vehicles/day).

• Crossing distance (continuous; ranging from 21 to 60 ft).

The data collection site within each city was included in the model as a random effect to account 

for differences among sites. Model estimation was done using PROC GLIMMIX in SAS. 

Stepwise backward-elimination was used to identify which factors and interaction were 

statistically significant at the 5-percent level. At each step, the factor, or interaction, with the 

highest p-value above 0.05 from a type 3 analysis F-test was excluded and the model rerun. A 

Type 3 analysis F-test determines the significance of each variable in the model individually. 

The results of this approach to obtain the final model, including beacon activation, city, and CDT 

as significant variables at the 0.05 level, are summarized in table 176.

Table 176. Stepwise elimination procedure results.

Step in

ANOVA

Type 3 p-value for each factor in the ANOVA model

Beacon

activation City

Beacon activation

times city interaction

CDT

(vehicles/day)

Crossing

distance

Step 1 < .0001 < .0001 0.40 0.15 0.94

Step 2 < .0001 < .0001 0.40 0.08 —

Step 3 < .0001 < .0001 — 0.07 —

Final < .0001 < .0001 — — —

p-values above 0.05 are not significant.

— Not Applicable.
182

Those variables significant at the 0.05 level—beacon activation and city—were included in the 

GLMM evaluation. The ANOVA results of the reduced model, containing beacon activation and 

city as statistically significant factors, are shown in table 177. The logistic regression results, on 

a logit scale, as well as the results of the type 3 analysis, are shown. The probability associated 

with the t-value (columns 6 and 7) indicates whether the corresponding coefficient is statistically 

significantly different from zero. The reference level (baseline) for this analysis was defined as 

non-activated beacons (of either type) in Milwaukee, WI. 

Table 177. GLMM results comparing driver yielding rates between activated and 

non-activated beacons. 

Effect Level

Coefficient

estimate

Standard

error DF t-value Pr > |t|

Type 3 

analysis 

F-value

Type 3 

analysis 

Pr > F

Intercept Intercept -1.51 0.26 8 -5.72 0.0004 — —

Beacon 

activation

Yes 1.30 0.08 2,433 15.79 < .0001 249.2 < .0001 Noa 0.00 — — — —

City

Austin 0.87 0.48 2,433 1.798 0.0723

16.07 < .0001

College 

Station 0.68 0.49 2,433 1.395 0.1630

Flagstaff 2.96 0.43 2,433 6.890 < .0001

Milwaukeea 0.00 — — — — a

Reference Level: City = Milwaukee; Beacon Activation = No.

— Not Applicable

Table 177 shows that, overall, more drivers yielded when the beacon was activated (coefficient 

on logit scale = 1.30) than when it was not activated (coefficient = 0). The table also shows that, 

overall, the percentage of drivers yielding was lowest in Milwaukee (coefficient = 0) and highest 

in Flagstaff (coefficient = 2.96). 

The predicted yielding rates and their 95-percent confidence limits are shown in table 178 for

each site in each city, separately when the beacon was activated or not activated, all other factors 

held constant. At each site, table 177 shows that the yielding rate is higher when the beacon was 

activated than when it was not activated (i.e., significant beacon activation effect shown in

table 177). Table 178 clearly shows that yielding rates in Flagstaff are considerably higher than 

in all other cities studied, regardless of whether or not pedestrians activated the device.

From the logistic model, the odds ratio for beacon activation was estimated. Because the 

interaction between beacon activation and city was not statistically significant, the odds ratio is 

the same for all cities (and sites in cities). The odds ratios and their 95-percent confidence limits 

are shown in table 179. In summary, drivers were, overall, 3.68 times more likely to yield when 

the beacon was activated than when it was not activated.
183

Table 178. Predicted driver yielding rates by city and site.

City, State Site

Beacon

activation

Driver yielding rate (percent)

Predicted

Lower 95-percent

confidence limit

Upper 95-percent

confidence limit

Austin, TX

AU-01 Yes 70 66 74

No 39 33 45

AU-02 Yes 61 56 66

No 30 25 35

College Station, 

TX

CS-01 Yes 78 71 83

No 49 39 58

CS-02 Yes 42 38 46

No 16 14 20

Flagstaff, AZ

FG-01 Yes 89 86 90

No 68 63 72

FG-02 Yes 95 93 97

No 84 78 89

FG-03 Yes 96 94 98

No 87 81 92

Milwaukee, WI

MK-04 Yes 45 40 49

No 18 15 21

MK-05 Yes 34 29 38

No 12 10 15

MK-06 Yes 53 49 57

No 24 20 27

MK-07 Yes 45 41 50

No 18 15 22

MK-08 Yes 47 43 52

No 20 16 24

Table 179. Odds ratio results for beacon activation.

Factor Comparison

Odds

ratio

Lower 95-percent

confidence

limit

Upper 95-percent

confidence

limit

Significantly different

from 1 at Alpha = 0.05?

Beacon Activation Yes versus No 3.68 3.13 4.32 Yes

Influence of Traffic Volume on Driver Yielding

During data collection at pedestrian crossings in the research, data collectors observed that some 

drivers appeared to make a last-minute decision to yield when a driver in the adjacent lane was 

yielding, and other drivers appeared to make a last-minute decision not to yield when a driver in 

the adjacent lane was not yielding. This behavior suggests that driver yielding might be 

influenced by other vehicles in the traffic stream. It has also been suggested that drivers might be 

less likely to slow down or stop to yield when there is traffic behind them; that is, when they feel 

“pushed” from behind. An analysis was conducted to explore whether the presence of other 

vehicles influenced driver yielding.

The objective of the analysis was to evaluate the relationship between traffic volume and driver 

yielding rate. To estimate traffic volume for a particular pedestrian crossing, 1-min traffic 

volume counts were obtained from the videos for a sample of the daytime data collection periods 
184

at RRFB sites. The 1-min counts were then aggregated into 5-min averages, but still expressed 

on a 1-min basis. For example, the 5-min average for a particular minute was the average of the 

actual count for that minute and the actual counts for the preceding 4 min. Statistics (sample size, 

minimum, and maximum) for 1-min volume counts at each site are shown in table 180.

Table 180. One-min volume count statistics at crossings with RRFBs.

City Site

Number 

of lanes

Number of crossing events 

with traffic present

Average of five 1-min volume counts

Minimum Maximum

Austin AU-01 2 37 12.0 18.8

AU-02 1 74 5.4 14.2

College Station CS-01 1 32 1.4 5.0

CS-02 2 48 13.0 28.0

Flagstaff

FG-01 2 134 17.0 36.0

FG-02 1 45 13.0 25.6

FG-03 2 32 8.8 15.8

Milwaukee

MK-04 2 87 4.2 10.6

MK-05 2 72 2.0 9.2

MK-06 3 63 10.4 18.6

MK-07 3 41 6.2 15.4

MK-08 2 76 4.0 12.0

The analysis was based on daytime study periods for which traffic counts were obtained (note 

that only RRFB study sites were considered), at both staged and nonstaged pedestrian crossings.

A GLMM with a binomial distribution and logit link was used to model the probability of driver 

yielding as a function of 1-min traffic volume count (representing the average 1-min count for 

the nearest 5-min period) and data collection site (to account for potential differences in traffic 

volume ranges and driver yielding rates between sites). Model estimation was done using PROC 

GLIMMIX in SAS.

The model showed that percentage yielding decreased as volume increased (slope estimate on 

the logit scale was -0.02278) but that relationship is not statistically significant (p-value = 0.30)

using a type 3 F-test. In other words, there is not enough evidence to conclude that traffic 

volume influences driver yielding behavior at sites with RRFBs in a positive or negative manner. 

This is also reflected in figure 101, which presents the plot of predicted driver yielding 

percentage versus average 1-min volume counts in a 5-min time period, separately for each site; 

the data points are color-coded by city; different symbols are used for the different sites in a city. 
185

Figure 101. Graph. Predicted percent of driver yielding by 1-min volume counts.

187

CHAPTER 7. SUMMARY/CONCLUSIONS, DISCUSSION, AND FUTURE RESEARCH 

NEEDS

OVERVIEW

The goal of this research effort was to improve pedestrian safety at urban and suburban crossing 

locations by identifying and evaluating low- to medium-cost pedestrian treatments. The 

treatments were to have the potential to reduce pedestrian crashes at both midblock and 

intersection locations. While several treatments were considered during early efforts of this 

project, later tasks focused on the RRFB. The RRFB has received extensive national attention 

because of high yielding rates observed at several installations. Several studies have found 

increased driver yielding after installing this device as summarized in table 181. The findings 

from this FHWA study are also provided in table 181.

SUMMARY OF PHASE I FINDINGS

Findings From Literature Review

Efforts in the initial phase of this project included a comprehensive literature review of 

pedestrian treatments being used at unsignalized intersections. The appendix to this report 

contains the entirety of the literature review of these treatments. Certain parts of the literature 

review were updated or additional literature reviews were conducted as needed to support work 

done in later tasks in this project. 

Review of Pedestrian Crash Data

A review of pedestrian crash datasets was conducted to document the characteristics, 

circumstances, and contributing factors for crashes at midblock pedestrian crossings and to 

assess the suitability of these databases for any safety evaluations to be conducted in the 

research. The following datasets were reviewed:

• NHTSA FARS.

• NHTSA GES.

• FHWA HSIS—included a review of data from the States of California, Minnesota, 

North Carolina, Ohio, Texas, and Washington.

FARS crash data were reviewed for the years 2005 through 2009, inclusive. During this period, 

22,892 pedestrian fatalities occurred in the United States, and 73.0 percent of the pedestrian 

fatalities occurred at midblock locations. The following statistics apply to the pedestrian fatalities 

that occurred at midblock locations:

• 1.3 percent occurred at marked midblock crosswalks.

• 30.7 percent occurred not in, but near, a marked midblock crosswalk.

• 68.0 percent occurred at locations that were not near marked midblock crosswalks.
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Table 181. Overview of driver yielding results from several RRFB studies.

Study Number of sites Driver yieldinga Unique characteristics of study

2010 FHWA(3) 22 (most in 

St. Petersburg, FL)

72 to 96 percent (stagedb) Original study that included data for 

multiple years

2009 FHWA(4) 2 (Miami, FL) 55 to 60 percent day (staged)

66 to 70 percent night 

(staged)

Day and night

2009 Florida(5) 1 (St. Petersburg, 

FL)

35 percent overall

54 percent activatedc

Trail crossing

2011 Texas(6) 1 (Garland, TX) 80 percent (staged) School, overhead

2011 Oregon(7) 3 (Bend, OR) 74 to 83 percent (staged) Two sites had 45 mi/h posted speed limit

2013 

California(8)

2 (Santa Monica, 

CA)

See table 182 Two sites where the RRFB and CRFB were 

alternately used. Data available for a third 

observation period where back plates were 

changed

2013 Calgary(9) 6 (Calgary, AB) 98 percent (staged) Before installing RRFB, the yielding was 

83 percent—type of before treatment not 

provided

2014 

Michigan(10)

1 (South Lyon 

Township, MI)

69 percent (staged) Comparison with no signs (20 percent), 

gateway in-street signs (80 percent), 

combination of gateway and RRFB 

(85 percent)

2014 

Texas(11,12)

22 (most in 

Garland, TX)

34 to 92 percent (staged) Significant variables: city, posted speed 

limit, crossing distance, one/two way

2014 

FHWA 

(this study)

12 daytime and 

4 nighttime (Austin 

and College 

Station, TX; 

Flagstaff, AZ, 

Milwaukee, WI)

Daytime (staged):

RRFB: 22 to 98 percent

CRFB: 36 to 95 percent

Study compared yielding with beacons 

with circular and rectangular shapes

Nighttime (staged):

RRFB: 53 to 95 percent

CRFB: 52 to 89 percent

Data were collected at night.

a

Range provided shows the average driver yielding for the sites included in the study as reported by the authors. See study 

reference for details regarding study methodology and whether the findings are significant.

b

Staged pedestrian was used to collect the data. c

Findings reported for when the device was activated (i.e., pedestrian pushed the pushbutton).

Table 182. Findings for 2013 Santa Monica, CA study.(8)

Shape Light

Range when activateda,b

(percent) Range when not activateda (percent)

RRFB Day 80–85 58–73

CRFB Day 63–92 57–83

RRFB Night 80–95 35–60

CRFB Night 65–90 35–80

RRFB Dusk 80–85 65–85

CRFB Dusk 55–100 20–75 a

Staged pedestrian was used to collect the data.

b

Findings reported for when the device was activated (i.e., pedestrian pushed the pushbutton).

In a review of a nationwide sample of crash data for all crash severity levels from GES, the 

database includes 10,079 crashes involving a pedestrian from 2005 to 2009, inclusive. Nearly 

half of the pedestrian crashes occurred at midblock locations. However, only 2.5 percent of the 

midblock pedestrian crashes were explicitly identified as midblock crossing crashes.
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HSIS data for California include crash data only for the State highway system, consisting of 

approximately 15,520 mi of highways. Data analyzed for this report included the years from 

2006 to 2008, inclusive. During the study period, 3,944 pedestrian crashes occurred on the 

California State highway system. Nearly 70 percent of the pedestrian crashes occurred at 

midblock locations. Only 2.6 percent of the midblock crashes were classified as occurring at 

midblock crosswalks.

HSIS data for Minnesota crash data include nearly all crashes statewide; including both Statemaintained and local-agency-maintained road systems. Data analyzed for this report included the 

years 2003 to 2007, inclusive. During the study period, 8,271 pedestrian crashes occurred in 

Minnesota. Approximately 29 percent of the pedestrian crashes occurred at midblock locations. 

Only 3.0 percent of the midblock crashes were classified as occurring at midblock crosswalks.

HSIS data for North Carolina include crash data for approximately 62,000 mi of the 77,000 mi of 

roadway on the State-maintained highway system. Data analyzed for this report included the 

years from 2005 to 2008, inclusive. During the study period, 3,847 pedestrian crashes occurred 

on the North Carolina State highway system. Nearly 85 percent of these pedestrian crashes 

occurred at midblock locations. Only 2.7 percent of the midblock crashes were classified as 

occurring at midblock crosswalks.

HSIS data for Ohio include crash data only for the State highway system, consisting of 

approximately 19,500 mi of highways. Data analyzed for this report included the years from 

2005 to 2008, inclusive. During the study period, 4,127 pedestrian crashes occurred on the Ohio 

State highway system. Approximately 45 percent of these pedestrian crashes occurred at 

midblock locations. Only 1.2 percent of the midblock crashes were classified as occurring at 

midblock crosswalks.

Data for Texas include crash data for both on and off the State highway system. Data analyzed 

for this report included the years 2003 to 2009, inclusive. During the study period, 

3,134,365 crashes were included in the Texas crash database. Of these, 39,993 (1.3 percent) were 

pedestrian crashes. Nearly 50 percent of the pedestrian crashes occurred at midblock locations. 

Only 136 crashes (0.7 percent of the midblock crashes) were classified as occurring at midblock 

crosswalks.

HSIS data for Washington include crash data only for the State highway system, consisting of 

approximately 7,193 mi of highways. Data analyzed for this report included the years from 2005 

to 2008, inclusive. During the study period, 1,573 pedestrian crashes occurred on the 

Washington State highway system. Nearly 40 percent of these pedestrian crashes occurred at 

midblock locations. Only 5.0 percent of the midblock crashes were classified as occurring at 

midblock crosswalks.

Local Field Observations

The research team made observations at selected midblock pedestrian crossings with a range of 

traffic control treatments. Ten midblock pedestrian crossings in 5 States were observed, 

including sites in 8 different cities. These observations were intended as a source of ideas about 

how particular crossing types could potentially be evaluated later in the study. The crossings 
190

observed were not selected as candidates for evaluation; indeed, many of the observed locations 

had already been treated in particular ways. The observation periods were typically brief (15 to 

30 min), and insights and assessments gained from these observations, by intention, should be 

regarded as anecdotal rather than definitive.

The limited field observations indicated that the inclusion of flashing lights on pedestrian 

crosswalk signs rather than just in the pavement surface appeared to substantially increase driver 

compliance with the law requiring yielding to pedestrians.

Selection of Studies for Phase II

The research team used the information gathered during the literature review and the crash 

evaluation and combined it with information provided by members of the Technical Advisory 

Panel to generate a list of five proposed crossing countermeasures that could be evaluated in

phase II of this FHWA project. The final selection of the phase II studies was made by the panel 

and representatives of FHWA during a face-to-face panel meeting and a later conference call. 

Refinements were made to the study plans during follow-on telephone calls with the panel and 

FHWA. The following two phase II studies were selected:

• Closed-course study with the following goal: identify the impacts of beacon shape, 

size, and placement on object detection in a closed-course setting.

• Open-road study with the following goal: identify driver yielding behavior to 

installed assemblies identified at the conclusion of the closed-course study. 

SUMMARY AND CONCLUSIONS FROM CLOSED-COURSE STUDY 

Traffic control options for a pedestrian crossing include numerous combinations of signs and 

flashing beacons/LEDs. To investigate the influence of beacon characteristics on drivers, 

participants drove on a closed course at the Texas A&M Riverside campus. During each lap, the 

participants viewed 8 study assemblies, 9 distractor signs, and up to 11 objects. The types of 

objects were a pedestrian (dressed in blue scrubs), a trash can, and a small gray box. The study 

assemblies included the following:

• Two circular 12-inch beacons located above the sign (named C-A12 in the study).

• Two circular 12-inch beacons located below the sign (C-B12).

• Two circular 8-inch beacons located below the sign (C-B8).

• One circular 12-inch beacon located above the sign and one circular 12-inch beacon 

located below the sign (C-V12).

• Two rectangular beacons located above the sign (R-A).

• Two rectangular beacons located below the sign (R-B), the format currently being 

used for the RRFB device.
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• Sign with LEDs embedded into the border (LED).

• Diamond-shaped sign with no beacons or LEDs (WO-B).

Specific research objectives for this closed-course study were the following:

• Determine whether the shape, size, and placement of flashing beacon/LEDs affect the 

following: 

o Sign legibility and symbol identification distances.

o Object detection. 

• Determine driver ratings of disability glare for 8-inch circular beacons and LEDembedded signs using a rapid flash pattern. 

• Identify up to two assemblies for field evaluation to be conducted following the 

conclusion of the closed-course tasks.

Driving Portion

Each participant drove the course twice with a pause between laps for the field crew to switch 

the signs and objects for the second lap. After the participants completed the driving portion of 

the study, they were directed to the discomfort glare portion of the study. The tasks for the 

participants while driving the route were to indicate when they could first do the following:

• See warning lights.

• See road signs.

• Read the words or identify the symbol on the road signs.

• See objects. 

As soon as the driver said “lights,” “sign,” “object,” or read the words/numbers/symbol on a road 

sign, the experimenter pressed a key on the laptop computer, which placed a mark in the file to 

indicate detection. Each sign on the course had two to three marks in the data file: one for 

detection distance of the sign, one for legibility distance, and (if lights were included on the sign 

or sign assembly) one for detection distance of the lights. Each object had one mark in the file.

Discomfort Glare Portion

At the beginning of the discomfort glare study, researchers asked the participants to park 250 ft 

away from sign 1. After the participant parked the vehicle, researchers turned on the beacon and 

asked the participant to indicate whether the brightness of the light was comfortable, irritating, or 

unbearable, which were defined as follows:

• Comfortable—when the glare is not annoying and the device is easy to look at.

• Irritating—when the glare is uncomfortable; however, the participant is still able to 

look at it without the urge to look away.
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• Unbearable—when the glare is so intense that the participant wants to avoid looking 

at it.

After the participant rated the first level, a technician increased the controller setting to level two. 

This process continued until the participant indicated the brightness was unbearable or the 

technician reached level six on the controller, the highest setting for the device. This process was 

then repeated at 150 ft for sign 1, 250 ft for sign 2, and 150 ft for sign 2.

Participants

The study recruited a group of participants approximately evenly distributed among males over 

55 years, females over 55 years, males under 55 years, and females under 55 years. Within each 

of those demographic groups, an even distribution between those who drove during the day and 

those who drove at night and between those who drove lap A first and those who drove lap B 

first was sought. These divisions resulted in 16 participant categories. The research goal was to 

have 4 participants in each of the 16 categories, resulting in 64 participants. The study included 

71 participants because participants were added to 1) replace participants whose data were not 

recorded successfully and 2) provide additional data to offset missing data points not collected 

because signs were temporarily disabled or objects were not appropriately placed.

Results

The evaluation of the driving portion of the study focused on the legibility distance for the study 

assemblies (i.e., the distance away from the sign when the participant could correctly state the 

words or symbol on the sign), the detection distance to objects, and the accuracy of detecting the 

objects. The discomfort glare evaluation focused on participants’ ratings of discomfort for an 

LED-embedded sign assembly and two circular 8-inch beacons with each having six different 

levels of intensity. 

Key Findings Regarding Legibility Distance

For the analysis that focused on legibility distance, which is the distance between the sign and 

the participant when the participant reads the message on the sign, results indicate the following:

• As expected, the legibility distance for signs during the day is greater than the 

legibility distance for signs at night. 

• Younger driver legibility distance is greater than older driver legibility distance. 

Finding age to be significant indicates future studies need to consider older 

participants.

• The type of assembly was significant at night and nearly not significant during the 

day. This indicates that the effects of the beacons/LEDs on reading the message on 

the sign are more influential during nighttime conditions, an expected finding.
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Key Findings Regarding Object Detection Distance

For the analysis focusing on object detection distance, which is the distance between the object 

and the participant when the participant said “ped,” “can,” or “box,” results indicated the 

following:

• As expected, there is a significant difference between day and night object detection 

distances. For example, the daytime detection distance to a pedestrian was on average 

911 ft with a standard deviation of 539 ft. During the night, the pedestrian detection 

distance had very different statistics: mean distance of 116 ft and standard deviation 

of 93 ft.

• Similar to legibility distance, there was a statistically significant difference owing to 

age during the daytime; surprisingly, the same finding did not occur at night. The 

nighttime condition seems to impede detection to a point that the effects of several 

variables are too small to detect in the experiment.

• Certain assemblies were associated with shorter object detection distances. For 

daytime conditions, the detection distance to an object was shorter for the R-B than 

with the C-B12, C-B8, or the R-A (statistically significant). During the nighttime, the 

detection distance to an object was shorter with the R-B than with the C-B12 

(statistically significant). These findings indicate that characteristics of the R-B, such 

as the light intensity or the location of the beacon beneath the sign, might negatively 

affect a driver’s ability to see an object.

Key Findings Regarding Object Detection Accuracy

For the analysis focusing on the accuracy of detecting objects, which considered the number of 

objects missed by the participants, the results indicate the following:

• As expected, there is a significant difference in the probability of missing objects 

between daytime and nighttime conditions. What was not expected was the 

magnitude of the difference. Overall, during the day, 1 in 23 pedestrians/trash cans 

were missed while at night 1 in 5 pedestrians/trash cans were missed. 

• For both daytime and nighttime conditions, the shape of the beacon did not matter; a 

similar probability of missing the object was present whether the beacons were 

circular or rectangular.

• The location of the beacons (above or below the sign) was significant during the day 

but not at night. During the day, participants were less likely to miss an object when 

the beacons were above the sign.

Key Findings for Discomfort Glare Study

The data show that for all devices at all distances, the percentage of participants indicating the 

brightness of the lights from the beacons/LEDS is comfortable decreases as brightness increases,

and the percentage of participants indicating the discomfort glare is unbearable increases as 

brightness increases. 
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The discomfort glare data indicate agencies should focus on meeting minimum intensity and 

place less emphasis on obtaining the brightest devices possible. When devices have intensities 

and optical powers close to the SAE minima, the probability of unbearable discomfort glare is 

less than 1 percent. 

SUMMARY AND CONCLUSIONS FROM OPEN-ROAD STUDY 

The open-road study was conducted to investigate 1) whether drivers yield differently to circular 

or rectangular beacons when used with a rapid-flashing pattern, 2) whether a driver is more 

likely to yield to a pedestrian when the rapid-flashing beacon is activated than when it is not 

activated, and 3) whether vehicle traffic volume affects driver yielding.

Both rectangular beacons and circular beacons were installed at 12 sites located in 4 cities 

(Milwaukee, WI; Flagstaff, AZ; Austin, TX; and College Station, TX). At half of the sites, the 

circular beacons were installed first while the rectangular beacons were installed first in the other 

half of the sites. The same flash pattern was used regardless if the beacons were circular or 

rectangular. The research team used a staged pedestrian protocol to collect driver yielding data to 

ensure that oncoming drivers receive a consistent presentation of approaching pedestrians.

Shape

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 ADT, random effect for rotation order, and the beacon shape variable.

(11,12) An 

indicator variable for nighttime conditions was included in the final model to determine whether 

nighttime results were significantly different from daytime results. The preliminary review of the 

findings (average daytime yielding was 63 percent for CRFBs and 59 percent for RRFBs)

indicates only minor, if any, differences between the CRFB and the RRFB. The results from the 

GLMM further revealed that there were no significant differences between the two beacon 

shapes (p-value =0.4717). For a subset of the sites, the brightness of the beacons was measured.

For those sites, there is clear evidence of an increasing yielding rate with increasing intensity at 

night. The trend is in the same direction during the day but with a smaller magnitude that the 

analysis found statistically insignificant.

Activation 

An analysis was also conducted to determine the extent to which the presence of an actively 

flashing beacon influences driver yielding. Driver yielding rates were compared between 

pedestrian crossings when a beacon was activated and pedestrian crossings when a beacon was 

not activated. The analysis included RRFBs and CRFBs, staged pedestrians and nonstaged 

pedestrians, and daytime study periods. The results of the analysis concluded that a driver is 

3.68 times more likely to yield when the beacon is activated than when the beacon is not 

activated. 
195

Traffic Volume 

Based on observations of driver behavior during the data collection, the research team conducted 

an analysis to determine whether driver yielding was influenced by other vehicles in the traffic 

stream. The objective of the analysis was to evaluate the relationship between traffic volume and 

driver yielding rate. The analysis focused on a 1-min vehicle count. To estimate the traffic 

volume present when a particular pedestrian was attempting to cross, 1-min traffic volume 

counts were obtained from the videos for a sample of the daytime data collection periods at 

RRFB sites. The 5-min period nearest to when the pedestrian was crossing was averaged. The 

results of the analysis concluded that traffic volume was not significant, suggesting that driver 

yielding behavior was not influenced by traffic volume present at the sites.

Results

In conclusion, traffic volume and the shape of a yellow rapid-flashing beacon do not have an 

impact on whether a driver yields to a pedestrian. However, while the shape of a beacon does not 

influence driver yielding, a driver is more than three times as likely to yield when a beacon has 

been activated as when it has not been activated. Other variables that had an impact on driver 

yielding include beacon intensity (for nighttime) and city (yielding was higher in Flagstaff 

compared with the other cities included in study).

DISCUSSION

The STC of the NCUTCD is interested in research findings that could assist in crafting language 

regarding this device that would result in material suitably generic for the MUTCD. For 

example, as studied in this research project, do the beacons need to be rectangular or could they 

be circular?

In this study, the presence of beacons or LEDs was associated with shorter nighttime sign 

legibility distances. One interpretation of this finding could be to question the use of beacons 

because they affect the ability of a driver to read a sign. Even if flashing beacons limit the ability 

to read a sign, their presence can warn drivers to take additional care at the location. The 

presence of a yellow flashing beacon communicates warning to a driver and, perhaps, the need to 

look for unexpected entries onto the roadway. Unfortunately, the extensive and continuous use of 

the flashing yellow beacon on roadways might not effectively communicate to drivers the needed 

action of slowing down or searching for a potential roadway entry. The use of a specific flash 

pattern, however, could offset some of these concerns. 

The brightness of the beacons 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. As the brightness of the beacons on a traffic control device increases, the 

probability of a driver indicating the discomfort glare is unbearable increases. When the 

discomfort glare is unbearable, drivers are more likely to divert their eyes away from the 

discomfort, which might result in drivers missing hazards located near the glare source. The 

profession needs to identify maximum brightness for RRFBs. The profession also needs 

guidance on whether to dim these devices during low light conditions, and if so, by how much. 
196

A part of the effort to set brightness levels is the need to investigate how best to measure the 

brightness of a flashing device. Peak intensity is not the only measure of brightness, and in some 

instances, it might not be the best measure of brightness. How to measure the impacts of 

different flash patterns that have unequal bright and dark periods must be considered. 

The closed-course study demonstrated that fewer objects were missed when the beacons were 

located above the sign. It also found that both the daytime and nighttime detection distance was 

shorter, which is less desirable, to objects beyond an assembly with two rectangular beacons 

below the sign compared with other selected assemblies. Therefore, based on these findings, 

having the rectangular beacons located above the sign rather than below the sign should be 

considered. 

The closed-course study also found that when grouping the beacons by shape (i.e., rectangular 

versus circular), there was no significant difference in object detection accuracy. Therefore, both

beacon shapes were selected for study in the open-road portion of the project, and results 

indicated that there are no significant differences in driver yielding between the two beacon 

shapes. With the finding from the open-road study that the shape of the yellow rapid-flashing 

beacon does not affect a driver’s decision to yield, agencies could have the flexibility to use 

either shape with their pedestrian treatment installations, assuming that the appropriate language 

is included in the MUTCD. Another interpretation is that with both shapes having similar 

yielding, one shape should be selected and specified in the MUTCD to promote pedestrian 

treatment consistency.

In the open-road portion of this FHWA study, the brightness of devices installed in the field was

also measured. 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. 

FUTURE RESEARCH NEEDS

In this section, a pedestrian rapid-flashing beacon assembly is assumed to consist of a Pedestrian 

Crossing (W11-2) or School Crossing (S1-1) sign and a pair of beacons (whether rectangular or 

circular) that flash in a rapid pattern. The rapid pattern could either be the 2-5 pattern, or the now 

preferred WW+S pattern as discussed in Official Interpretation #4(09)-41 (I)—Additional Flash 

Pattern for RRFBs.

(43) The rapid-flashing beacon assembly can be located either roadside or 

overhead. 

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 rapidflashing beacons used at pedestrian crossings are discussed in the following sections.

Appropriate Use of Rapid-Flashing Beacon Assemblies on Only One Side of the Roadway 

Approach

The original Interim Approval for the RRFB requires the following for assembly location: 

For any approach on which RRFBs are used, two W11-2 or S1-1 crossing warning 

signs (each with RRFB and W16-7P plaque) shall be installed at the crosswalk, one 

on the right-hand side of the roadway and one on the left-hand side of the roadway. 
197

On a divided highway, the left-hand side assembly should be installed on the median, 

if practical, rather than on the far left side of the highway.”(1)

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.

When Rapid-Flashing Beacons Should Be Dimmed, and by How Much

The profession needs guidance 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 

rapid-flashing beacons. 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.

Appropriate Brightness Level of Rapid-Flashing Beacons

The brightness of the beacons 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. The results of this study indicate the profession might want 

to consider a maximum brightness for beacons used with pedestrian crossing signs—and the 

maximum brightness should vary between daytime and nighttime conditions.

Appropriate Installation of Rapid-Flashing Beacon 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.

(13) The catalyst for this 

interpretation was a concern that a frequent bus presence at a near-side bus stop would 

unacceptably obscure approaching road users’ view of the Pedestrian Crossing (W11-2) signs 

mounted at the normal roadside locations. 

When Garland, TX, installed several RRFBs on multilane undivided roadways, city staff were 

concerned that the left-side roadside assembly would be outside the driver’s cone of vision or it 

could easily be obscured by a truck traveling in the opposite direction. Medians on divided 

roadways provide a location for a left-side beacon installation adjacent to traffic approaching the 

crosswalk, but roadways with narrow or no medians do not. Therefore, at crosswalks located on 

undivided roadways (e.g., four lanes and a TWLTL, multilane one-way roads) or roadways with 

a median less than 4 ft wide, Garland installed RRFBs and School Crossing signs on a mast arm 

over the roadway. Garland also decided to supplement its overhead installation with roadside 

installations as illustrated in figure 2.

Presence of buses and street width are two examples of site conditions where the rapid-flashing 

beacon could be installed overhead rather than roadside, but there might be other criteria that 
198

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. Additional research is needed to investigate these questions.

Guidance on Selection of Appropriate Pedestrian Crossing Treatment for a Particular 

Location

In general, the pedestrian hybrid beacon has higher yielding rates but costs more than rapidflashing beacon assembly. The rapid-flashing beacon is more effective than many other 

pedestrian treatments; however, a Texas study found lower compliance for the RRFB for longer 

crossing distances.

(11) 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, and/or 

others). It could start with the methodology developed as part of NCHRP 562/Transit 

Cooperative Research Program (TCRP) 112, which uses pedestrian delay to make the 

determination of whether to recommend a device with a red indication (e.g., pedestrian hybrid 

beacon), a yellow indication (i.e., an active device such as a rapid-flashing beacon), or a 

crosswalk.

(44) The method also includes a step to determine whether a traffic control signal is 

warranted. Figure 102 shows an illustration of a graph that can be generated from the NCHRP 

562/TCRP 112 methodology using an assumed crossing distance and other variables. The user 

would then use the major road volume and the pedestrian volume to determine the appropriate 

type of pedestrian treatment for the site. The graph in figure 102 is out of date because the 

research was done prior to the 2009 MUTCD change in the pedestrian signal warrant, but the 

concept is applicable.
199

Figure 102. Graph. Example of a graph generated from NCHRP 562/TCRP 112

methodology (function of walking speed, crossing distance, and other variables) that could 

be used to determine pedestrian treatment.

(44)

National Education Campaign on the Rapid-Flashing Beacon

What education campaigns have been used by cities and jurisdictions that have implemented 

rapid-flashing beacons? Were they successful? 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, cautions against distracted walking, walking during 

nighttime conditions, blind spots around commercial vehicles, and others. Education campaigns 

could be directed toward drivers, pedestrians, or both.

Minimum Number of Pedestrians to Warrant a Pedestrian Treatment

There is a growing use of the pedestrian hybrid beacon 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 pedestrian hybrid beacon, and these graphs include a minimum of 

20 pedestrians per h.(2) When deciding to recommend this minimum pedestrian number, the 

National Committee based its decision on a value developed through engineering judgment 

during an FHWA study on whether to mark crosswalks.(45)

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 
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should have a higher minimum number compared with a crossing that is more than 0.25 or 

0.5 mi 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 

induced pedestrian crossing maneuvers (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. 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 also 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.

Pedestrians’ Attitude 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 when it was provided. Some of 

those pedestrians were jaywalking and 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 decision-making and examine why pedestrians who have the 

opportunity to use a treatment (such as a rapid-flashing beacon) 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.

Influence of Traffic Volume on Driver Yielding

In this research, an analysis was conducted using RRFB sites to evaluate the relationship 

between traffic volume and driver yielding rate. While the plot in figure 101 suggests that the 

percentage of driver yielding might decrease as volume increases, that relationship was not 

statistically significant. In other words, there was not enough evidence to conclude that traffic 

volume influences driver yielding behavior at sites with RRFBs in a positive or negative 

direction. However, the percentage of driver yielding varied substantially between cities, and this 
201

city-to-city variability might have had a stronger influence on the model than traffic volume. 

Additional research with larger sample sizes and/or additional cities is needed to look at the 

relationship between traffic volume and driver yielding more closely.

Estimating Pedestrian Exposure

With average daily vehicle traffic 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—what are the most effective means for obtaining pedestrian exposure? 

203

APPENDIX: PEDESTRIAN TREATMENTS

A variety of engineering (e.g., geometric design, traffic control device) treatments are available 

with the potential of improving safety at pedestrian crossings. Research studies have been 

conducted across the United States and in a number of other countries to understand better the 

effects of these treatments. This appendix contains summaries of a selection of treatments, along 

with reported results on their effectiveness. The list of treatments considered for this report is 

provided in table 183.

Table 183. Pedestrian treatments for unsignalized locations.

Treatment Included in this appendix? CMF available?

Advance stop or yield line and sign Yes None

Barrier—median Yes None

Barrier—roadside/sidewalk (railing or fencing) Yes None

Bus stop location Yes None

Circular beacons Yes None

Crosswalk marking patterns Yes None

Curb extensions Yes None

Flags (pedestrian crossing) Yes None

Illumination Yes Yesa,b

In-roadway warning lights Yes None

In-street pedestrian crossing signs Yes None

Marked crosswalk Yes Yesb

Motorist warning signs Yes None

Overpasses and underpasses Yes None

Pedestrian hybrid beacon (PHB) (also known as HAWK) Yes Yesb

Puffin crossing Yes Yesb

Raised crosswalks Yes Yesb

Rectangular rapid flashing beacon (RRFB) No—see chapter 2 None

Refuge island Yes None

Road diet Yes None

Sidewalks Yes None

Zigzag lines Yes None

Leading pedestrian interval No—signal treatment Yesb

No right turn on red No—signal treatment Yesa,b

Pedestrian countdown No—signal treatment Yesb

Pedestrian scramble No—signal treatment Yesb

Signal No—signal treatment None a

Highway Safety Manual.

(46)

b

FHWA CMF Clearinghouse.

(47)

Puffin = Pedestrian User-Friendly Intelligent.

CMF = Crash Modification Factor.

ADVANCE STOP OR YIELD LINE AND SIGN

Advance yield lines (i.e., pavement markings) place the traditional stop or yield line 30 to 50 ft 

upstream of the crosswalk and are often accompanied by YIELD HERE TO PEDESTRIAN 

signs. Advance yield lines address the issue of multiple-threat crashes on multilane roadways, 

where one vehicle stops for a pedestrian in the crosswalk but inadvertently screens the pedestrian 

from the view of vehicles in other lanes. Several studies have documented that advance yield 
204

lines decrease pedestrian–vehicle conflicts and increase driver yielding at greater distances from 

the crosswalk. (See references 48 through 51.)

Studies by Van Houten and others have demonstrated the effectiveness of advance yield lines 

and YIELD HERE TO PEDESTRIAN signs.

(49,50,51) This research found a marked reduction in 

motor vehicle–pedestrian conflicts and an increase in motorists yielding to pedestrians at 

multilane crosswalks with an uncontrolled approach. These results have been documented at 

crosswalks with and without amber flashing beacons. Van Houten and Malenfant also 

demonstrated that the markings and sign together were more effective than the sign alone.

(50) In a 

2001 study by Van Houten et al., advance yield lines and YIELD HERE TO PEDESTRIAN 

signs were shown to reduce vehicle–pedestrian conflicts by 67 to 87 percent.

(51) The study also 

found a large increase in the distance at which motorists yielded to pedestrians. These evaluation

results were further replicated at 24 additional study sites located in Canada.

(52) Results showed 

that the advance yield sign and advance yield markings reduced the percentage of motor vehicle–

pedestrian conflicts involving evasive action and increased the percentage of motorists yielding 

to pedestrians and yielding further back from the crosswalk line. Treatments were applied only to 

streets posted at 30 mi/h.

A 2011 paper reported on the installation of advance yield markings with a YIELD HERE TO 

PEDESTRIAN sign at two midblock locations in Las Vegas.

(53) Results indicated that there was 

an increase in the proportion of drivers yielding to pedestrians at the location with a five-lane 

cross section, an ADT of 17,100 vehicles/day, and a posted speed limit of 35 mi/h. The increase 

in driver yielding was not statistically significant at the location with seven-lane cross section, an 

ADT of 43,000 vehicles/day, and a posted speed limit of 30 mi/h.

BARRIER—MEDIAN

Placing a barrier in a median is a pedestrian crossing treatment discussed in a review of 

pedestrian safety research by Campbell et al.

(54) The purpose of barriers in the median is to 

discourage pedestrians from crossing at undesirable locations and encourage them to cross at a

crosswalk. As part of a larger test of various pedestrian countermeasures, median fence barriers 

were installed at two sites: one in Washington, DC, with a 4-ft fence, and one in New York City, 

with a 6-ft fence.

(55) The median fence barrier at one site consisted of two gaps, each located at 

an intersecting minor street. After installation of the barrier, researchers interviewed pedestrians 

to gauge their reactions to the treatment. Regarding crosswalk use, a reported 61 percent of the 

pedestrians identified the barrier as the reason for using the crosswalk. When asked whether the 

barrier affected the manner in which they crossed the street, 52 percent stated it had no effect, 

while 48 percent indicated the only effect was to force them to cross at the intersection. Of those 

who were crossing midblock before the installation, 61 percent did so out of convenience, and 

about one-third indicated they would use the crosswalk only if midblock traffic volumes were 

“very heavy.” After the fence was installed, 32 percent of the 22 pedestrians interviewed who 

previously made midblock crossings stated inconvenience as the major factor, with high turning 

volume at the intersection as a close second (23 percent). In particular, older pedestrians were 

generally concerned with turning traffic at intersections, and many cited recent crash experience

as a concern. Almost one-quarter of those interviewed indicated they had walked along the 

median to the end of the barrier, or an opening, before completing the crossing. While merchants 

at a control site indicated they did not anticipate much effect from a median barrier, 58 percent of 
205

those at the experimental sites indicated that its major effect was to discourage customers from 

shopping both sides of the street. Most residents accepted the barrier, only 7 percent wanted it 

removed, and a few complained about inconvenience and unsightly appearance.

BARRIER—ROADSIDE/SIDEWALK 

A recent FHWA International Scan found that pedestrian railings were common in the United 

Kingdom, where they were used to direct pedestrian movements to preferred crossing locations 

at intersections and in median islands.

(56) They also offered a useful guide to pedestrians with 

visual disabilities. The railings appeared to be most common in areas with high pedestrian traffic.

Campbell et al. discuss several studies in which chains, fences, guardrails, and other similar 

devices were proposed as a means of channelizing and protecting pedestrians. (See references 54

and 57 through 60.) Parking meter post barriers were tested at three urban areas sites.

(55) All of 

the tests used chains that connected parking meter posts. The barrier was 3 ft high and 

incorporated as many as three chains. In Washington, DC, six parking meter post barriers were 

created on one side of a street, resulting in a series of 12-ft single chain sections. In New York 

City, 19 posts were used, 9 on one side of the street and 10 on the other. These were 12-ft 

sections with two chains. The third site was a section of one-way street along which three-chain 

sections were installed on eight posts. Results of the study were mixed, in part because of

vandalism (i.e., stolen chains) that interfered with the experiment. Twenty-six percent of those 

interviewed who crossed at the intersections after the installation mentioned that a factor in their 

choice of crossing location was that it was illegal to cross elsewhere. Because only 12 percent of 

those interviewed had mentioned this before the change, the authors surmised that the barriers 

may have reminded pedestrians that it was illegal to jaywalk. While 65 percent of merchants 

perceived no negative effects from the countermeasure, 15 percent noted that the chains 

interfered with street crossings, and 18 percent cited a problem when loading or unloading 

goods. 

In London, research was conducted on an 1,800-ft road segment with pedestrian barriers on both 

sides. Access openings on each side of the road were not located directly across from each 

other.

(54,61) Researchers mapped pedestrian crossing movements and compiled crash data from 

the site. Crashes during the previous 8 years were shown as a ratio to a 4-hr pedestrian volume, 

which was fewer than 20,000 people. The resulting risk ratio was compared with that for 

11 other sites in London that did not have pedestrian barriers. The only significant difference in 

risk ratio occurred at midblock crossings located within 150 ft of a signalized intersection (these 

locations had more than twice the risk ratio with the pedestrian barrier) and at other midblock 

locations within 60 ft of an intersection (where controlled crossings were not present and had 

approximately 10 times the risk ratio). The overall risk ratio was lower at the test site but was not 

found to be statistically significant. Researchers also studied the longitudinal path taken by each 

pedestrian; this path was the distance between barrier openings used to get on and off the 

roadway, measured parallel to the curb. The results indicated most pedestrians would cross 

outside of the crosswalk when the longitudinal distance between barrier openings on either side 

of the street was less than 30 ft. The author suggested that longitudinal distances between the 

openings on opposite sides of a street should be greater than 30 ft. 
206

Pedestrian barrier fences were installed along 18 sections of roadway in Tokyo.

(62) Analysis of 

crashes before and after the installation revealed that crashes related to crossing pedestrians 

declined by nearly 20 percent. Researchers observed an overall reduction of 4 percent, including 

non-pedestrian crashes. It had been thought that even though crashes related to pedestrians 

crossing out of crosswalks might decrease, crashes related to pedestrians crossing in the 

crosswalks might increase. However, the results indicated that both types of pedestrian collisions 

were reduced by an equal percentage.

BUS STOP LOCATION

TCRP Report 125: Guidebook for Mitigating Fixed-Route Bus-and-Pedestrian Collisions 

provides information on pedestrian–bus crashes and countermeasures and strategies for reducing 

these crashes.

(63) Lack of pedestrian-friendly environments was noted as one of the factors. This 

includes sidewalk conditions such as broken and uneven sidewalks, narrow sidewalks, sidewalk 

obstacles, and lack of sidewalks or other positive separation. Lack of lighting was another 

concern noted.

According to Campbell et al., 2 percent of all pedestrian collisions in urban areas can be 

classified as pedestrian collisions at bus stops.(54) Most do not involve a pedestrian being struck 

by a bus, but the bus creates a visual screen between approaching drivers and pedestrians 

crossing in front of the bus. In rural areas, pedestrian crashes related to school bus stops were 

identified in 3 percent of all pedestrian crashes. A countermeasure proposed for urban crashes 

involved relocating bus stops to the far side of intersections to encourage pedestrians to cross 

behind rather than in front of the bus. This allows the pedestrian to be seen and to see oncoming 

traffic closest to the bus. To determine the effect of such relocation on pedestrian crossing 

behavior, two before–after studies evaluated bus stop relocations. One site in Miami, FL, was

located on a two-way, four-lane street intersecting with a two-way, two-lane street at an 

unsignalized location. The other site was located in San Diego, CA, on a two-way, four-lane 

street intersecting with a one-way, three-lane street at a signalized intersection with pedestrian 

signals.

(54,55) The relocation of the bus stops to the far side eliminated the undesired crossing 

behavior; previously, half of the riders crossing the street after disembarking crossed in front of 

the bus.

An analysis of pedestrian crashes in Sweden found school bus stops should be located with 

greater consideration of pedestrian safety factors.

(54,64) Campbell reported that Swedish 

researchers drew the following conclusions about the location of bus stops:

• Ensure they are not hidden by vegetation or other obstacles.

• Place them away from roadway curves or superelevated locations.

• Provide adequate standing and playing area for the waiting passengers.

• Provide maximum sight distance to all critical elements.

Additional guidance for the location and design of bus stops is provided in TCPR Report 19.

(65)
207

CIRCULAR BEACONS

The use of circular flashing beacons for pedestrian crossings is prevalent in the United States. In 

some instances, there are concerns that the overuse of flashing beacons or the continuous 

flashing at specific locations has diluted their effectiveness in warning motorists of a pedestrian 

crossing. Flashing beacons have been installed in a variety of ways, including the following:

• At the pedestrian crossing, both overhead and side mounted.

• In advance of the pedestrian crossing, both overhead and side mounted.

• In conjunction with or integral within other warning signs.

• In the roadway pavement itself (see section on in-roadway warning lights).

The operations for flashing amber beacons may also vary, including the following: 

• Continuous flash mode.

• Pedestrian-activated using manual pushbuttons.

• Passive pedestrian detection using automated sensors (e.g., microwave, video).

• Different flash rates, sequences, or strobe effects.

The experience with flashing beacons has been mixed, as would be expected when they have 

been installed in numerous ways. Several studies have shown that intermittent (typically 

activated using a manual pushbutton or automated sensor) flashing beacons provide a more 

effective response from motorists than continuously flashing beacons.

(66,67) These beacons do not 

flash constantly; thus, when they are flashing, motorists can be reasonably assured that a 

pedestrian is crossing the street. With pedestrian activation, special signing may be necessary to 

ensure that pedestrians consistently use the pushbutton activation. Alternatively, automated 

pedestrian detection has been used with some success but typically requires extra effort in 

installation and maintenance.

Overhead flashing beacons appear to have the best visibility to motorists, particularly when used 

both at and in advance of the pedestrian crossing. Many installations have used both overhead 

and side-mounted beacons. The effectiveness of the flashing beacons in general, however, may 

be limited on high-speed or high-volume arterial streets. For example, overhead flashing beacons 

have produced driver yielding behavior that ranges from 30 to 76 percent, with the median 

values falling in the mid-50 percent range; however, the evaluations did not contain enough 

information to attribute high or low driver yielding values to specific road characteristics. (See 

references 48, 66, 67, and 68.) The field studies conducted in a TCRP/NCHRP project 

(documented in TCRP Report 112/NCHRP Report 562) found a similarly wide range of motorist 

yielding values (25 to 73 percent), with the average value for all flashing beacons at 

58 percent.

(44) The analysis of site conditions and traffic variables also found that traffic speeds, 

traffic volumes, and number of lanes have a statistically significant effect on driver yielding 

behavior on arterial streets.

Little and Saak evaluated two installations of pedestrian-activated overhead yellow flashing 

beacons.

(69) Both sites consisted of a five-lane cross section and ADTs of either 7,500 or 

18,400 vehicles/day. The motorist compliance at the sites was 64 to 65 percent during the day 

and 68 to 78 percent at night.
208

Van Winkle and Neal evaluated the use of pedestrian-actuated advance and crosswalk flashers in 

Chattanooga, TN.

(66) The installation of the crosswalk flashers was a compromise solution for a 

group of senior citizens that demanded a traffic signal so that they could cross a minor arterial 

street with speed limit of 40 mi/h. City staff conducted a before-and-after study in 1987, with 

follow-up data collection in 2000. Data were collected on the percentage of drivers yielding or 

slowing at the pedestrian crosswalk. The original 1987 data showed that driver yielding 

improved from 11 to 52 percent in the eastbound direction and 6 to 32 percent in the westbound 

direction. The percentage of drivers yielding at this location has been sustained as a long-term 

improvement; driver yielding in 2000 was measured to be 55 percent in the eastbound direction 

and 45 percent in the westbound direction. The authors attribute the success of the flashers to 

pedestrian actuation. The city of Chattanooga has installed similar flashing crosswalk warning 

devices at three other locations with what it characterizes as similar results, although no formal 

studies of their effectiveness have been conducted.

Sparks and Cynecki reported in 1990 on the use of flashing beacons for warning of pedestrian 

crosswalks in Phoenix, AZ.

(70) The city evaluated the application of advance warning flashing 

beacons at four pedestrian crossing locations. The authors describe the use of several 

experiments in their evaluation, including before-and-after speed and crash data collection as 

well as treatment-and-control experiments for traffic speeds. The authors found that the advance 

warning flashing beacons did not decrease speeds or crashes, and in some cases, the traffic 

speeds or crashes increased after installation of the flashing beacons. These findings led the 

authors to conclude the following:

...[F]lashers offer no benefit for intermittent pedestrian crossings in an urban 

environment. In addition, the longer the flashers operate the more it becomes part of 

the scenery and loses any effectiveness.(70) (p. 35)

The authors do concede that actuated warning flashers may be beneficial in a high-speed rural 

environment with unusual geometrics, high pedestrian crossings, and unfamiliar drivers;

however, these conditions were not tested in their study.

CROSSWALK MARKING PATTERNS

In a 2009 FHWA study of crosswalk markings, researchers investigated the relative daytime and 

nighttime visibility of three crosswalk marking patterns: bar pairs, continental, and transverse 

lines.

(31) In the study, conducted on the campus of Texas A&M University in College Station, 

TX, information was collected on the distance from the crosswalk at which 78 participant 

motorists verbally indicated visual recognition of the crosswalk with the different patterns. The 

participants were about evenly divided in gender between males and females and in age between 

younger than 55 years old and older than 55. 

The researchers used instrumented vehicles on a route along open roads on the campus. The 

research team collected data during two periods: daytime (sunny and clear or partly cloudy) and 

nighttime (street lighting on). The tests used existing markings (six intersection and two 

midblock locations) and new markings installed for the study (nine midblock locations). 
209

For the study sites, the findings indicate that the marking type (bar pair, continental, or 

transverse) was statistically significant. The detection distances to bar pairs and continental 

markings were statistically similar, and they were statistically longer than the detection distance 

to the transverse markings, both during the day and at night. 

For the existing midblock locations, the drivers detected the continental markings at about twice 

the distance upstream as the transverse markings during daytime conditions. This increase in 

distance translates to 8 s of increased awareness of the presence of the crossing at 30-mi/h

operating speeds.

The participants also rated the appearance of markings on a letter-grade scale of A to F. The

researchers compared those subjective ratings of visibility for all the groups and variables 

identified in the preceding analysis. The ratings for bar pairs and continental were consistent 

over various comparison groups, with better ratings for bar pairs and continental markings than 

for transverse markings. These results mirrored the findings from the evaluation of detection 

distances. Overall, participants preferred the continental and bar pairs markings over the 

transverse markings.

The research team worked with the NCUTCD to develop recommendations for incorporating the 

findings from the study into the MUTCD. The recommendations were endorsed on June 23, 

2011. Figure 103 shows the proposed figure for inclusion in the next edition of the MUTCD.

Figure 103. Diagram. Examples of crosswalk markings (figure proposed to replace existing 

MUTCD figure 3B-19).(71)
210

A 2010 paper presented the findings from an empirical Bayes evaluation of high-visibility school 

(yellow, continental-style) crosswalks in the city of San Francisco, CA.

(72) The analysis used data 

for 54 treated intersections with high-visibility crosswalks and 54 control intersections, each 

chosen for its geographical proximity to a treated intersection. The study found a statistically 

significant reduction in crashes of 37 percent for intersections with high-visibility school 

crosswalks. 

CURB EXTENSIONS 

The purpose of a curb extension, also known as a choker, curb bulb, or bulbout, is to reduce the 

width of the vehicle travel way at either an intersection or a midblock pedestrian crossing 

location. It shortens the street crossing distance for pedestrians, may slow vehicle speeds, and 

provides pedestrians and motorists with an improved view of one another, thereby reducing the 

risk of a motor vehicle-pedestrian collision. Campbell et al. identify multiple studies of 

variations of this treatment in Australia, the Netherlands, and Canada.(54)

In two Australian cities (Keilor, Queensland, and Eltham, Victoria), researchers indicated that 

“curb blisters” had little effect on reducing vehicle speeds.(54,73) However, in Concord, New 

South Wales, researchers compared a subarterial street treated with both curb blisters and marked 

parking lanes to an untreated street; the comparison showed that the crash rate on the treated 

street was only one-third that of the untreated street. The number of these crashes involving

pedestrians was not stated, nor is it known how the streets compared before treatment.

Australia’s “wombat” crossings usually consist of a raised platform with a marked crosswalk on 

top, and a refuge and curb blisters where space permits. Thus, they combine features of both 

speed tables and bulbouts. They are designed to slow motorists, shorten pedestrian exposure to 

motor vehicles, and increase pedestrian visibility to motorists. Reports of those studies indicate 

that wombat crossings have generally reduced 85th percentile vehicle speeds by 40 percent.

(54,73)

The Dutch towns of Oosterhout and De Meern installed variations of street-narrowing 

treatments. The Oosterhout project consisted of installing two bulbouts so as to require motorists 

to deviate from a straight path. Both the 85th percentile vehicle speed and the degree of 

pedestrian–motor vehicle conflict fell after the deviation was installed. De Meern’s path 

deviation was created by placing two bulbouts opposite one another to narrow the width of the 

traveled way. Researchers did not observe a significant reduction in the 85th percentile vehicle 

speed, and opinions of the treatment were mixed. Residents did not express a strong sense of 

neighborhood improvement, swerving cars were thought to endanger bicyclists, school teachers 

thought that children would be confused by the deviation, retailers were concerned about 

accessibility and parking, and there was some concern about emergency vehicle access.

(54,74)

Macbeth reported favorable speed changes seen on five raised and narrowed intersections and 

seven midblock bulbouts (two raised) in Canada.

(54,75) The speed limit was also lowered to 

19 mi/h. The results of the speed changes are presented in table 184.
211

Table 184. Speed changes due to bulbouts.

(54)

Period

Percent exceeding

19 mi/h 25 mi/h 31 mi/h

Before 86 54 13

After 20 3 2

Huang and Cynecki reported in 2001 the effects of bulbouts at various locations to determine 

their effects on selected pedestrian and motorist behaviors.

(76) At four intersections in 

Cambridge, MA, and Seattle, WA, they found no significant effect on motorists yielding to 

pedestrians in crosswalks, as shown in table 185.

Table 185. Percentage of motorists yielding to pedestrians at bulbout crosswalks.

(76)

Location Locations

Beforea

(percent)

Aftera

(percent) Significance

Cambridge, MA 2 20.0 (5) 66.7 (6) Smallb

Seattle, WA 2 57.9 (342) 52.2 (471) Noc

a

Sample size in parentheses. b

Small sample size. c

Not significant at 0.10 level.

Huang and Cynecki used a treatment-and-control study approach to evaluate four additional 

bulbouts in Greensboro, NC, and Richmond, VA. Because of low pedestrian activity in both 

Greensboro and Richmond, it was necessary to stage pedestrian crossings, using a two-person 

data collection team. Motorists stopped for fewer than 10 percent of the staged pedestrians in 

both cities. The differences between the treatment and control sites were not statistically 

significant at the 0.10 level, as shown in table 186.

Table 186. Percentage of motorists stopping for staged pedestrians at bulbout 

crosswalks.

(76)

Location Locations Treatmenta Controla Significance

Greensboro, NC 2 5.2 (211) 7.6 (185) Nob

Richmond, VA 2 0.0 (66) 0.0 (66) Nob

a

Sample size in parentheses. b

Not significant at 0.10 level.

FLAGS

Several cities (e.g., Salt Lake City, UT; Kirkland, WA; Berkeley, CA) use fluorescent orange 

flags that are carried by crossing pedestrians. The research team found no formal studies in the 

literature on the effectiveness of crossing flags; however, anecdotal information has indicated 

that these crossing flags are effective in improving driver yielding behavior. The flags in Salt 

Lake City are used mostly on streets near the downtown area that have speed limits of 30 mi/h or 

less. Several of these streets, however, are multilane, high-volume arterials. Field studies 

conducted in a TCRP/NCHRP project (documented in TCRP Report 112/NCHRP Report 562) 

found pedestrian crossing flags in Salt Lake City and Kirkland were moderately effective.

(44) The 

study sites with crossing flags had motorist yielding rates that ranged from 46 to 79 percent, with 

an average of 65 percent compliance. Several of the study sites had four or more lanes with 

speed limits of 30 mi/h or 35 mi/h.
212

ILLUMINATION 

At certain locations, site characteristics can make a crosswalk difficult for the driver to see at 

night or in dusk/dawn settings. Trees, shadows, or glare from nearby buildings, and roadway 

alignment can all affect the ability of approaching drivers to see a crosswalk or pedestrians who 

use it. Adding illumination can improve the visibility and the safety of such crosswalks. 

Campbell et al. discuss three studies on illumination in Australia, Israel, and the United States, 

which are summarized in the following paragraphs.(54)

Pegrum conducted a two-stage study of lighting of pedestrian crossings in Perth, Australia.

(54,77)

A pilot study showed sufficient success to initiate a broader scale lighting program, in which 

63 sites were studied. The illumination consisted of two luminaires (100-watt sodium lamps), 

one on each side of the roadway at either side of the crosswalk, mounted approximately 12 ft 

from the crosswalk at a height of 17 ft and aimed at a point 3 ft above the pavement. Campbell 

states that Pegrum reported the sodium floodlighting resulted in a significant decrease in 

nighttime pedestrian crashes; a summary of crashes is shown in table 187.

Table 187. Crash effects of providing sodium floodlights at pedestrian crossings in

Perth, Australia.

(54)

Test Study Period

Pedestrian crashes (fatalities)

Vehicle-only crashes 

(fatalities)

Day Night Total Day Night Total

Pilot Test: 

6 crossings

5 years before 19 (1) 7 (1) 26 (2) 5 (0) 1 (0) 6 (0)

5 years after 21 (1) 2 (0) 23 (1) 9 (0) 0 (0) 9 (0)

Follow-On Test:

57 additional crossings

2 years before 57 (2) 32 (1) 89 (3) 19 (0) 2 (0) 21 (0)

2 years after 58 (2) 13 (1) 71 (3) 18 (1) 1 (0) 19 (1)

Polus and Katz developed and tested a combined illumination and signing system for pedestrian 

crosswalks in Israel.

(54,78) Reported changes in nighttime crashes at the 99 illuminated study sites 

and 39 unilluminated control sites are summarized in table 188, which shows a noticeable 

decrease in nighttime crashes at the study sites compared with increased crashes at the control 

sites. The authors concluded that crash reductions were primarily the result of the illumination, 

because daytime crashes were largely unchanged. Campbell also states that the authors studied 

other possible influences, including changes in pedestrian and vehicle flow, weather differences, 

and national crash trends, but none showed any effect on the results.

Freedman et al. conducted a study in Philadelphia to assess the impacts of installing improved 

lighting at seven sites.

(54,79) The impacts were evaluated on the basis of behavior as measured for 

728 pedestrians and 191 drivers at the 7 study sites and 7 control sites. The researchers reported 

that the study sites were high-crash locations, while the control sites were low-crash locations. 

The illumination improvement consisted of 90-watt low-pressure sodium lamps. Each system 

was controlled by a photocell that energized the circuit at sundown and turned it off at sunrise, 

with a provision for experimenter override.
213

Table 188. Effects of crosswalk illumination on nighttime pedestrian crashes in Israel.

(54)

Location

Nighttime crashes

Before After

Illuminated Study Sites (99) 28 16

Unilluminated Control Sites (39) 10 16

The Philadelphia evaluation contained a comparison of changes in five pedestrian attributes—

search behavior, crossing path, concentration, erratic behavior, and clothing brightness—before 

and after lighting improvements. According to Campbell’s summary of the researchers’ report, 

the comparison showed that “perceived clothing brightness” increased significantly after 

installing the special illumination.(54) Observers who searched the street in a manner similar to 

drivers perceived the general appearance of pedestrians as brighter. The researchers also reported 

significant improvement in the apparent concentration of pedestrians to the crossing task at all 

signalized locations, and search behavior was found to improve significantly under all 

conditions. Drivers appeared more aware of approaching crosswalks when the illumination was 

present. Campbell added a note that changes in the number of crashes at both groups of sites 

moved toward the mean, consistent with what would be expected because one group consisted of 

high-crash sites and the other consisted of low-crash sites; however, he reported that the 

behavioral measures should not have been influenced by regression to the mean. 

A recent study in Las Vegas by Nambisan et al, evaluated a midblock crosswalk illumination 

system with automatic pedestrian detection devices.

(80) The “smart lighting” system detected the 

presence of pedestrians that were using the crosswalk and activated additional lighting during 

their time within the crosswalk. This strategy was used to address problems related to motorists’

failure to yield and the high proportion of nighttime crashes, and it was thought to be more 

effective in capturing the attention of approaching drivers than the use of continuous highintensity lighting in the crosswalk. 

Using a before-and-after methodology, the researchers studied the results of the “smart lighting” 

test based on two MOE categories: safety MOEs, including pedestrian and motorist behaviors, 

and mobility MOEs, consisting of pedestrian and vehicle delay. Results indicated that safety 

MOEs improved, as shown in table 189 and table 190. The percentage of increase in the diverted 

pedestrians from the before to the after condition was reported as statistically significant, as was 

the decrease in the proportion of pedestrians trapped in the roadway and the improvement in 

motorist yielding behavior.

Table 189. Results for “smart lighting” pedestrian safety MOEs.

(80)

Measure of effectiveness

Before (n = 44) After (n = 84)

Number Percent Number Percent

Pedestrians who look for vehicles before 

beginning to cross 44 100 84 100

Pedestrians who look for vehicles before 

crossing second half of street 44 100 84 100

Diverted pedestrians 0 0 14 17

Pedestrians trapped in roadway 13 13 12 14
214

Table 190. Results for “smart lighting” motorist safety MOEs.

(80)

Measure of effectiveness

Before (n = 91) After (n = 116)

Number Percent Number Percent

Motorists yielding to pedestrians 20 22 41 35

Distance motorist stops/yields 

before crosswalk (ft)

0–10 8 40 16 39

10–20 10 50 16 39

> 20 2 10 9 22

IN-ROADWAY WARNING LIGHTS AT CROSSWALKS

As a specific design case of flashing beacons, in-roadway warning light installations have 

proliferated since the 1990s. Their use originated in California and Washington State but has 

spread to numerous other cities in the United States. In-roadway warning lights are mounted in 

the pavement near the crosswalk markings such that they typically protrude above the pavement 

less than 0.5 inch. As with flashing beacons, the experience with in-roadway warning lights has 

been mostly positive but with a few negative results.

Many early and some current equipment designs for the in-roadway warning lights have been 

problematic. Some of the problems encountered are as follows:

• Snow plows damage the flashing light enclosures.

• Light lenses become dirty from road grit and require regular cleaning.

• Automated pedestrian detection does not operate effectively.

Many of the early problems have been resolved through experience, but some cities continue to 

be cautious in specifying more in-roadway warning lights until they have long-term experience. 

Some cities have noted their preference for overhead flashing beacons instead of in-roadway 

lights because of poor visibility issues when traffic is queued in front of the in-roadway 

lights.

(67,81) Another concern is that in very bright sunlight, the flashing lights are difficult for 

drivers to see.

In-roadway warning lights have been evaluated in numerous studies with varying results. It 

appears that the effectiveness of this treatment varies widely depending on the characteristics of 

the site and existing motorist and pedestrian behavior. 

For most of the installations, in-roadway warning lights have increased driver yielding into the 

50- to 90-percent range. (See references 68 and 82 through 86.) In addition, the in-roadway 

warning lights typically increase the distance that motorists first brake for a pedestrian crossing, 

indicating that motorists recognize the pedestrian crossing and the need to yield sooner. (See 

reference 82 through 85.) These results have been even more dramatic at night when the inroadway warning lights are highly visible. For a few installations, driver yielding decreased or 

did not increase above 35 percent.

(68,69,87) The research team did not include in-roadway warning 

lights in the early 2000 TCRP/NCHRP project’s field studies because of the abundance of 

evaluation results in the literature.

(44)

On the walkinginfo.org website, Thomas provided a review of an in-roadway warning light 

(IRWL) system.

(88) Nine studies were identified that provided some evaluation of potential safety 
215

effects, all using behavioral MOEs. The following results were noted from the review performed 

by Thomas(88)

:

• Short-term improvements in motorist yielding to pedestrians were reported from most 

sites studied. No improvement or improvement only to low levels was reported for a 

number of locations, approaches, or study conditions. (See references, 86, 87, 89, 

and 90.)

• Trends (from two studies) indicated greater improvements at nighttime; however,

effects under other sub-optimal visibility conditions, such as rain or fog, have not 

been clearly studied.

(86,91)

• There were inconsistent results (between two studies) on whether IRWL improves 

yielding to pedestrians in the middle of their crossing.

(68,89) This MOE may have a 

greater bearing on safety than yielding for pedestrians waiting or just beginning to 

cross, but not yet in the path of vehicles. The effect of IRWL on those in the middle 

of their crossing, particularly for multilane roads should be further studied. In the 

meantime, caution should be exercised, and perhaps additional treatments 

implemented, if IRWL is considered for uncontrolled crosswalks at multilane 

locations.

• Reported effects on motorist speeds were also mixed, with studies finding the 

following:

o Improvements or slight improvement in speeds.

(86,92,93)

o No improvement.

(85,89)

o Mixed results for some locations and study conditions.

(86,89)

• Effects on conflicts between motorists and pedestrians using the crosswalk also 

varied, along with the definitions of conflicts used in the studies. Authors reported the 

following:

o A non-significant increase in conflicts in one study.

(92)

o Reduced conflicts at all four locations in a study from Israel.

(90)

o Reduced conflicts following installation of high-visibility crosswalks and 

sidewalk improvements, but no improvement related to the IRWL in one 

study.

(94)

o Fewer conflicts were observed among those using the IRWL crosswalk 

compared with those crossing at other locations (after period only).

(89)

• Longer-term data are generally lacking. When data were available, improvements in 

yielding and other measures were typically greatest at the shortest after-interval 

measured, with worsening trends seen at later time intervals.

(86,89,94) Thus, the 

potential for a degradation of initial improvements is suggested, and the treatment 
216

should be monitored at repeated intervals over a year or more. Certainly any available 

crash data and characteristics should be considered.

• Most of the studies included one treatment site, and none included comparison sites to 

control for time-related trends or other unknown factors. Confounding treatments and 

other conditions were also noted in several of the studies. Most of the studies 

determined only short-term effects of the treatment, having examined the effects for 

intervals from a few weeks to several months post-implementation.

The following paragraphs provide additional details for a sample of the evaluations available for

in-roadway warning lights.

Whitlock and Weinberger Transportation, Inc., in 1998, summarized the evaluation results of inroadway warning lights at numerous locations in California.

(82) In these installations, the inroadway warning lights were supplemented with a pedestrian crosswalk sign with warning amber 

LED lights, as well as a pedestrian-activated pushbutton with flashing LEDs and a CROSS 

WITH CAUTION sign. Two different MOEs were used to report evaluation results: 

1) percentage of motorists yielding to pedestrians and 2) advance vehicle braking distance. For 

all six study sites, the percentage of motorists yielding to pedestrians increased after treatments 

were installed; daytime yielding improved from 28 to 53 percent, and nighttime yielding 

increased from 13 to 65 percent. The improvements in motorist yielding behavior and the actual 

percentage of yielding motorists were typically much greater for nighttime conditions. The 

changes in advance vehicle braking distance showed similar results, with increases in braking 

distance being greater during nighttime conditions.

The city of Kirkland, WA, installed in-roadway warning lights at two midblock locations in the 

fall of 1997.

(84) Whitlock and Weinberger Transportation, Inc. evaluated the crossing treatments 

at these locations and reported the results using the same two MOEs as the California study. The 

evaluation team found improvements to both MOEs after installation, with more dramatic 

improvements evident during nighttime tests. Before installation, nighttime driver yielding 

ranged from 16 to 65 percent. After installation of the in-roadway warning lights, yielding 

increased to a range of 93 to 100 percent. Daytime yielding improved from 46 to 64 percent 

before treatment to 85 to 94 percent after. The study found the following:

“The concept of amber flashing lights embedded in the pavement at uncontrolled 

crosswalks clearly has a positive effect in enhancing a driver’s awareness of 

crosswalks and modifying driving habits to be more favorable to pedestrians.”(82)

(p. 1)

Boyce and Van Derlofske compared the effectiveness of in-roadway warning lights to basic 

crosswalk markings at a single location with two crosswalks in Denville, NJ.

(94) The authors 

found that the in-roadway warning lights decreased the speed at which vehicles approached the 

crosswalk, but that this speed reduction diminished over time. In addition, vehicle–pedestrian 

conflicts with the in-roadway warning lights also increased over time. The authors also reported 

several problems with this specific implementation of in-roadway warning lights. 
217

Katz, Okitsu, and Associates in 2000 prepared a study of in-roadway warning lights for Fountain 

Valley, CA.

(85) Their study analyzed the reported safety record of approximately 30 treatment 

locations that were in place for more than 1 year and compared it with the expected safety record 

for traditional crosswalk treatments. The system appears to have reduced the crash expectancy by 

80 percent; however, it is not known whether this is a novelty effect or will continue over time. 

The study also found that marked crosswalks with in-roadway flashers had a lower crash rate 

than comparable marked crosswalks.

Huang et al. documented in 2000 the evaluation of in-roadway warning lights at a single location 

in Orlando, FL.

(87) The evaluation, which was conducted to determine the effects of the inroadway warning lights on pedestrian and motorist behavior, collected both before-and-after and 

treatment-and-control data. The authors reported the following results:

• Average vehicle speeds decreased by 1.9 mi/h when a pedestrian was present and 

0.8 mi/h when no pedestrians were present, but the decreases were not significant.

• Vehicle yielding improved from 13 percent before to 34 percent (when flashers were 

activated) and 47 percent (when flashers were not activated) after installation. The 

authors could not explain why more drivers yielded when the flashers were not 

activated.

• About 28 percent of the pedestrians crossed in the flashing crosswalk when police 

officers were not present. The remaining 72 percent of pedestrians crossed elsewhere, 

depending on what was the most convenient path between their origins and 

destinations.

• Of the pedestrians who crossed in the flashing crosswalk, 40 percent did not 

experience any conflicts, compared with 22 percent of those who crossed within 30 ft 

and only 13 percent of those who crossed elsewhere. The researchers concluded that 

motorists were more likely to stop or slow for pedestrians who crossed in or near the 

flashing crosswalk than for those who crossed elsewhere.

In a subsequent study, Huang evaluated in-roadway warning lights at two uncontrolled 

pedestrian crossings—one in Gainesville, FL, and one in Lakeland, FL.

(68) The evaluation used 

traditional before-and-after data collection and used the following MOEs: 1) motorists yielding 

to pedestrians, 2) pedestrians who had the benefit of motorists yielding to them, 3) pedestrians 

who crossed at a normal walking speed, and 4) pedestrians who crossed in the crosswalk. The 

results for these MOEs were quite different between the two study sites. At the study site in 

Gainesville, driver yielding actually decreased from 81 to 75 percent. Although the decrease was 

significant, it was considered practically negligible because of site characteristics. At the 

Lakeland site, driver yielding improved from 18 to 30 percent; this result was not statistically 

significant because of low sample sizes. The results from the other MOEs were not that 

informative because major changes were not observed. 

Prevedouros in 2001 reported on the evaluation of in-roadway warning lights installed on a sixlane arterial street in Honolulu, HI.

(86) The evaluation consisted of a traditional before-and-after 
218

study of traffic volumes, vehicle spot speeds, pedestrian crossing observations, and pedestrians’ 

and motorists’ perceptions of change in the situation. The author reported the following results:

• A 16- to 27-percent reduction in vehicle speeds was measured when the flashing 

lights were activated.

• The average pedestrian wait time at the curb decreased from 26 to 13 seconds, and the 

average crossing time decreased from 34 to 27 seconds. The crossing time decreased 

because pedestrians did not have to wait as long in the refuge island before crossing 

the second direction.

• The proportion of pedestrians who were observed to run during the crossing 

decreased from 22 to 12 percent after the flashing lights were installed. The 

proportion of pedestrians crossing outside the marked crosswalk also decreased from 

16 to 8 percent after installation.

IN-STREET PEDESTRIAN CROSSING SIGNS

In-street pedestrian crossing signs (2003 MUTCD R1-6 and R1-6a signs) are intended for use at 

uncontrolled (unsignalized) crosswalks. The signs can be installed on the centerline or in the 

median with either a portable or fixed base. Because the signs are located between the lanes, they 

can have a traffic-calming effect from the narrowing of the lanes.

A 2009 report documented the findings from three area-wide countermeasure programs 

implemented in Las Vegas, NV; Miami-Dade, FL; and San Francisco, CA.

(4) The three field 

teams used different applications of the in-street pedestrian signs in terms of location and number 

of signs used. The signs proved to be very effective in increasing driver yielding (see table 191). 

Driver yielding increased between 13 and 46 percent depending on the location. There were no 

significant changes in the percentage of pedestrian–vehicle conflicts at the Miami sites or at two 

of the three sites in San Francisco. Only one location (Mission & Admiral) in San Francisco 

experienced a significant decrease in pedestrian–vehicle conflicts. Conflicts were reduced from 

17.1 percent in the baseline to 2.1 percent after installation of the signs. 
219

Table 191. Driver yielding at in-street installations.

(4)

Site

Before

number

After

number

Before—

percent of 

drivers yielding 

to pedestrians

After—percent 

of drivers 

yielding to 

pedestrians

Percent 

change p-value

Miami: Collins & 6th 400 440 32 78 46 0.01

Miami: Collins & 9th 400 240 21 65 44 0.01

Miami: Collins & 13th 1,200 200 34 69 35 0.01

San Francisco: 16th & Capp 

(marked crosswalk)

519 447 61 74 13 < 0.01

San Francisco: 16th & Capp 

(unmarked crosswalk)

96 109 40 60 20 < 0.01

San Francisco: Mission & 

France

164 91 43 78 35 < 0.01

San Francisco: Mission & 

Admiral

41 47 22 57 35 < 0.01

Las Vegas: Bonanza 

between D and F

89 106 74 47 -27a > 0.05

Las Vegas: Twain between 

Cambridge and Swenson

141 79 7 35 18 < 0.01

a

Counterintuitive result—results are not significant because this is a one-tailed test.

A 2007 study compared the effect on driver yielding behavior resulting from the installation of 

in-street pedestrian crossing signs. The signs were placed at three positions relative to the 

crosswalk—at 0 ft, 20 ft, and 40 ft in advance of the crosswalk—and three study sites were 

evaluated in the study.(4) The data showed that the sign produced a marked increase in yielding 

behavior at all three study sites and that installation of the sign at the crosswalk line was as 

effective as or more effective than installation of the sign 20 or 40 ft in advance of the crosswalk. 

The data also indicated that placement of the sign at all three locations at once was no more 

effective than placement of the sign at the crosswalk line. These data suggest that the in-roadway

sign is likely effective because the in-roadway placement is particularly salient to drivers. 

Because drivers frequently struck the signs at one of the sites, the authors recommended that 

these signs be placed on median islands whenever possible to extend their useful lives.

In-street pedestrian crossing signs were examined in the TCRP/NCHRP project.

(44) The field 

studies indicated that in-street signs had relatively high motorist yielding (ranged from 82 to 

91 percent, for an average of 87 percent); all three study sites were on two-lane streets with 

posted speed limits of 25 or 30 mi/h. 

A 2011 paper reported on installations of in-street pedestrian crossing signs at three midblock 

locations in Las Vegas.

(53) The results either 1) showed a decrease in motorist yielding or 2) were 

not statistically significant. The signs were installed on roads with 35-mi/h speed limits, five- or 

seven-lane cross-sections, and ADTs between 17,100 and 21,400 vehicles/day. The wide 

crossing may have contributed to the decrease in motorist yielding.

To improve pedestrian safety at a relatively low cost, the Pennsylvania Department of 

Transportation has a program to provide Yield-to-Pedestrian Channelizing Devices (YTPCD) to 

municipalities. YTPCDs are placed on the centerline of a roadway in advance of marked 

crosswalks to remind motorists of the need to yield to pedestrians. A research report by Strong 

and Kumar and a paper by Strong and Bachman summarized an evaluation of these devices.

(95,96)
220

Behavioral data were collected in 2006 in four different community types (urban, suburban, 

small city, and college town) before and after installation. Sites included crosswalks at 

unsignalized intersections (eight sites) and midblock locations (four sites). Speed limits at all 

sites were either 25 or 35 mi/h. Data were analyzed with respect to whether motorists were more 

likely to yield to pedestrians. The analysis showed a statistically significant increase in motorist 

yielding. Table 192 provides the results from the study along with findings reported from other 

studies. 

A series of treatments were installed at a bike trail crossing site in Michigan in a study that 

examined the effectiveness of a “gateway” in-street sign configuration with the RRFB used alone 

and in combination.(10) Because of a sharp curve, the posted speed was 25 mi/h, and there were 

two through lanes (one in each direction) and a center turn lane. When the signs were absent and 

the RRFB not activated, yielding averaged 20 percent. The RRFB alone produced an average 

yielding level of 69 percent. The gateway in-street sign treatment, which consisted of in-street 

signs on the lane line on both sides of the turn lane and on each side of the road, produced 

80 percent yielding. The combination of the gateway in-street sign configuration and RRFB 

produced 85 percent yielding. The authors concluded that the data showed that the gateway instreet signs produced effects that were similar to the RRFBs and that the combination of gateway 

in-street signs and RRFB may produce effects similar to the gateway in-street signs alone, which 

suggests that the gateway in-street signs can be more cost effective than the more expensive 

RRFBs.
221

Table 192. Evaluation results on in-street pedestrian crossing signs.

Location

Measure of 

effectiveness Result Reference

Miami, San 

Francisco, 

Las Vegas

Motorist Yielding Before range of 7 to 

74 percent

After range of 35 to 

78 percent

Between 13 and 

46 percent increase

Pécheux, K., Bauer, J., and McLeod, P. 

Pedestrian Safety and ITS-Based 

Countermeasures Program for Reducing 

Pedestrian Fatalities, Injury Conflicts, and 

Other Surrogate Measures Draft Zone/AreaWide Evaluation Technical Memorandum. 

Contract #DTFH61-96-C-00098; Task 9842. 

2009.

TCRP/ 

NCHRP

Motorist Yielding With signs = 82 to 

91 percent

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

P., Ullman, B., Trout, N., Park, E.S., Whitacre,

J., Lalani, N., and Lord, D. Improving 

Pedestrian Safety at Unsignalized Crossings. 

TCRP Report 112/NCHRP Report 562. 2006.

Pennsylvania

(Philadelphia 

Haverford 

Township, 

Pottstown, 

and West 

Chester)

Motorists 

Yielding—

Intersection 

Locations

Before = 27 percent

After = 59 percent

Increase = 30 to 

34 percent

Strong, C., and Kumar, M. Safety Evaluation of 

Yield-to-Pedestrian Channelizing Devices.

Western Transportation Institute. Montana State 

University. 2006.

Strong, C., and Bachman, D. Safety Evaluation 

of Yield-to-Pedestrian Channelizing Devices in 

TRB 87th Annual Meeting Compendium of 

papers DVD. 2008.

Motorists 

Yielding—Midblock 

Locations

Before = 10 percent

After = 30 percent

Increase = 17 to 

24 percent

Previous studies as reported by Strong and Kumar or Strong and Bachmann(95,96)

New York 

State and 

Portland, OR

Pedestrians for 

Whom Motorists 

Yielded

+12 percent Huang, H., Zegeer, C., Nassi, R., and Fairfax, 

B. The Effects of Innovative Pedestrian Signs at 

Unsignalized Locations: A Tale of Three 

Treatments, Report No. FHWA-RD-00-098, 

Federal Highway Administration, Washington, 

DC. 2000.

Pedestrians Who

Ran, Aborted, or 

Hesitated

-2 percent

Pedestrians Crossing 

in Crosswalk

No change

Cedar 

Rapids, IA

Motorists Yielding +3 to 15 percent Kannel, E.J., Souleyrette, R.R., and Tenges, R.

In-Street Yield to Pedestrian Sign Application 

in Cedar Rapids, Iowa, Center for 

Transportation Research and Education, Iowa 

State University, Ames, IA. 2003.

Minnesota Speed Compliance +20 percent Kamyab, A., Andrle, S., and Kroeger, D.,

Methods to Reduce Traffic Speed in High

Pedestrian Areas, Report 2002-18, Prepared for 

the Minnesota Department of Transportation, 

St. Paul, MN. 2002.

Madison, WI Motorists Yielding +5 to 15 percent City of Madison Traffic Engineering Division, 

Year 2 Field Evaluation of Experimental “InStreet” Yield to Pedestrian Signs. City of 

Madison Department of Transportation,

Madison, WI. 1999.

ITS = Intelligent transportation system.

TCRP = Transit Cooperative Research Program.

NCHRP = National Cooperative Highway Research Program.
222

MARKED CROSSWALKS

Zegeer et al. have performed the most authoritative study to date on the effectiveness of 

crosswalk pavement markings alone as a pedestrian crossing treatment at uncontrolled 

locations.

(45,97) Five years of pedestrian collisions at 1,000 marked crosswalks and 1,000 matched 

unmarked comparison sites in 30 U.S. cities were analyzed. The study concluded that no 

meaningful differences in crash risk exist between marked and unmarked crosswalks on two-lane 

roads or on low-volume multilane roads. The study indicated that as traffic volumes, speeds, and 

street widths increase, crosswalk markings alone are associated with a greater crash frequency 

than no crosswalk markings. The study recommendations indicate that the issue should not be 

whether to provide crosswalk markings on these high-volume, high-speed streets. Instead, the 

recommendations point to the necessity of using other treatments in addition to crosswalk 

markings that will provide a safer street crossing for pedestrians.

Koepsell et al. in 2002 published a study of the effects of crosswalk markings on the risk of 

vehicle–pedestrian crashes involving older pedestrians.

(98) The study gathered crash data and 

other site characteristics (e.g., traffic and pedestrian volumes, traffic speed, signalization 

characteristics) from six cities in Washington State and California from 1995 to 1999. The study 

used a case-control design and compared 282 case sites with 564 control sites. After adjusting for 

the various traffic and pedestrian characteristics, the researchers found that the risk of a 

pedestrian–vehicle crash was 3.6 times greater at uncontrolled intersections with a marked 

crosswalk. At intersections with a stop sign or traffic signal, there was “virtually no association 

between presence of markings and pedestrian-motor vehicle collision risk.”

Knoblauch, Nitzburg, and Siefert reported on a study of the effects of pedestrian crosswalk 

markings on pedestrian and driver behavior.

(99) The study included 11 unsignalized intersections 

in four cities: Sacramento, CA; Richmond, VA; Buffalo, NY; and Stillwater, MN. The 

researchers considered the following behavior in the crosswalk markings evaluation:

• Pedestrian compliance with crossing location.

• Vehicle speeds.

• Vehicle yielding compliance.

• Pedestrian behavior as related to level of caution.

The authors presented the following conclusions:

• Drivers appeared to drive slower when approaching a marked crosswalk. The speed 

reductions are modest but evident nonetheless. This finding implies that most 

motorists are aware of the pedestrian crossing.

• No changes in driver yielding behavior were observed after the installation of marked 

crosswalks. This result implies that motorists may be slowing down just in case they 

are forced to stop by a pedestrian stepping into the roadway.

• There were no changes in blatantly aggressive pedestrian behavior after installations 

of marked crosswalks, indicating that pedestrians do not feel overly protected by 

marked crosswalks.
223

• Overall, crosswalk usage increased after marked crosswalks were installed. The 

authors found that single pedestrians are more likely to use marked crosswalks than a 

group of pedestrians traveling together.

Gibby et al. analyzed pedestrian-vehicle crash data at 380 intersections on California State 

highways.

(100) The study found that crash rates at marked crosswalks were 3.2 to 3.7 percent 

higher than crash rates at unmarked crosswalks (after accounting for pedestrian exposure). This 

result corresponded to earlier work by Herms in San Diego, and also correlates to Zegeer’s study 

in the late 1990s. The implication is that marked crosswalks alone are not sufficient on multilane 

streets with high traffic volumes and speeds.

In the late 1960s, Herms examined 5 years of crash experience at 400 unsignalized intersections 

in San Diego, CA.

(101,102) The study found that nearly six times as many crashes occurred in 

marked crosswalks as in unmarked crosswalks. After accounting for crosswalk usage, the crash 

ratio was reduced to about three times as many crashes in marked crosswalks. Many have 

criticized this study as leading to the removal of pedestrian accommodation on city streets. Many 

now think that crosswalk markings should not be removed in these cases, but rather 

supplemented with various other types of safety treatments that enable pedestrians to cross busy 

roadways.

MOTORIST WARNING SIGNS

Pedestrian crossing signs were installed at several locations in the Miami metropolitan area.

(4)

The signs were tested at a midblock section of Collins Avenue in Miami. Collins Avenue has a 

two-lane cross section with on-street parking, an ADT of 29,500 vehicles/day, and a speed limit 

of 30 mi/h. Following installation of pedestrian crossing signs, there were no significant changes 

in average vehicle speed or the percentage of drivers braking when a pedestrian was present. No 

conflicts were observed in the before or after conditions. The operating speed at the site in the 

before condition was 10 mi/h below the posted speed limit of 30 mi/h, which was suggested as a 

reason that a speed change was not observed.

Huang et al. evaluated three innovative pedestrian signing treatments at locations in Seattle, WA; 

six sites in New York State; Portland, OR; and three sites in Tucson, AZ.

(103) The three 

treatments evaluated were an overhead crosswalk sign, a pedestrian safety cone typically placed 

in the roadway, and an overhead flashing regulatory sign prompting motorists to stop for 

pedestrians in the crosswalk. The evaluation used traditional before-and-after data collection for 

three MOEs: 1) percentage of pedestrians for whom motorists yielded; 2) percentage of 

pedestrians who ran, aborted, or hesitated; and 3) percentage of pedestrians crossing in the 

crosswalk. The results of the study are shown in table 193. All treatments except the overhead 

flashing sign in Tucson resulted in improvements in motorist yielding. The authors indicated that 

the effectiveness of the flashing regulatory sign may have been limited because it was installed 

on four- and six-lane arterial streets with speed limits of 40 mi/h. (The other study locations were 

primarily two-lane streets with speed limits of 25 or 30 mi/h.) 

High-visibility signs and markings were examined in the TCRP/NCHRP project (documented in 

TCRP Report 112/NCHRP Report 562) in 2004.

(44) The results demonstrated the effect of higher 

posted speed limits. One site with high-visibility signs and markings and a posted speed limit of 
224

25 mi/h had a motorist yielding value of 61 percent. However, the other two study sites with 

high-visibility signs and markings and a posted speed limit of 35 mi/h had motorist yielding 

values of 10 and 24 percent, for an average of 17 percent.

Table 193. Effectiveness of pedestrian treatments at unsignalized locations.

(103)

Study Location

Percent of pedestrians 

for whom motorists 

yielded

Percent of pedestrians 

who ran, aborted, or 

hesitated

Percent of pedestrians 

crossing in the crosswalk

Overhead crosswalk sign, 

(1 site in Seattle)

Before—46

After—52

Before—58

After—43

Before—100

After—100

In-roadway pedestrian 

safety cone (6 sites in New 

York, 1 site in Portland)

Before—70

After—1

Before—35

After—33

Before—79

After—82

Overhead flashing 

crosswalk regulatory sign 

(3 sites in Tucson)

Before—63

After—52

Before—17

After—10

Before—94

After—94

OVERPASSES AND UNDERPASSES

Pedestrian overpasses (bridges) and underpasses (tunnels) allow pedestrians and bicyclists to 

cross streets while avoiding potential conflicts with vehicles.

(104) Because they are expensive to 

construct, grade separated crossings should be reserved for locations where there is high demand 

for crossings by pedestrians, bicycles, and individuals with physical disabilities, and the risks of 

crossing the roadway are high. Ideally, overpasses and underpasses should take advantage of the

topography of a site—grade separations are less expensive to construct and more likely to be 

used if they can help pedestrians avoid going up and down slopes, ramps, and steps. 

Zegeer et al. discussed several grade separation treatment studies.

(54) An analysis was made of 

reported pedestrian crashes for 6 months before and 6 months after the installation of pedestrian 

overpasses at 31 locations in Tokyo, Japan.

(54,62) The overall results are shown in table 194. The 

table shows data for 656-ft sections and 328-ft sections on either side of each site. Crashes 

determined to be “related” to the treatment (assumed to be pedestrian crossing crashes) 

decreased substantially after overpass installation, but non-related crashes increased by 

23 percent in the 656-ft sections. There was also a greater reduction in daylight pedestrian 

collisions than nighttime collisions.

The effectiveness of pedestrian overpasses and underpasses depends a great deal on their level of 

use by pedestrians. A 1965 study by Moore and Older found that use of overpasses and 

underpasses depended on walking distances and convenience of the facility.

(54,105) They defined a 

convenience measure (R) as the ratio of the time to cross the street on an overpass divided by the 

time to cross at street level. The researchers found that approximately 95 percent of pedestrians 

will use an overpass if the walking time in using the overpass is the same as crossing at street 

level (i.e., R = l). However, if crossing using the overpass takes 50 percent longer than crossing 

at street level (R = l.5), almost no one will use the overpass. Usage of pedestrian underpasses 

was not as high as overpasses for similar values of R.
225

Table 194. Comparison of crashes before and after installation of pedestrian overpasses

in Tokyo.

(54)

Type of Crash

656-ft sections 328-ft sections

Before After

Reduction

(percent) Before After

Reduction

(percent)

Related crashes 2.16 0.32 85.1 1.81 0.16 91.1

Non-related crashes 2.26 2.77 -22.9 1.65 1.87 -13.7

Total 4.42 3.09 29.9 3.46 2.03 41.1

Accessibility must also be considered when designing grade-separated crossings. A panel of 

people with disabilities was asked to comment on accessibility issues after using three pedestrian 

overpasses in San Francisco, CA.

(54,106) They identified the following major elements as creating 

a barrier or hazard to the user with disabilities:

• Lack of adequate railings to protect pedestrians from drop-offs on overpass 

approaches.

• Greater than acceptable cross slopes.

• No level area at the terminals of the ramps on which to stop wheelchairs before 

entering the street.

• Lack of level resting areas on spiral bridge ramps.

• Railings difficult to grasp for wheelchair users.

• Lack of sight distance to opposing pedestrian flow on spiral ramps.

• Use of maze-like barriers to slow bicyclists on bridge approaches that create a barrier 

to those who use wheelchairs or who are visually impaired.

• Lack of sound screening on the bridge to permit people with visual impairments to 

hear oncoming pedestrian traffic and otherwise more easily detect direction and avoid 

potential conflicts.

A 1980 study by Templer et al. investigated the feasibility of accommodating pedestrians with 

physical disabilities on existing overpass and underpass structures.(54,107) A review of 

124 crossing structures revealed that 86 percent presented at least one major barrier to the 

physically handicapped; the most common barriers were the following:

• Stairs only (i.e., no ramps for wheelchair users) leading to an overpass or underpass.

• Ramp or pathway to ramp that is too long and steep.

• Physical barriers along the access paths on structure.

• Sidewalk on the structure that is too narrow.

• Cross slope on the ramp that is too steep.

Various solutions to these access problems were developed and assessed based on cost 

effectiveness. The Americans with Disabilities Act has since required the barriers to wheelchair 
226

users to be removed, requiring more gentle slopes and periodic level areas for wheelchair users 

to rest. While use of these gentle slopes also makes it easier for bicyclists and other users, it has 

also greatly increased the length of ramps, which may discourage usage. Methods such as 

carefully planned fencing have been used to channel pedestrians to the overpasses and 

underpasses to increase usage and discourage potentially risky at-grade crossings.

PEDESTRIAN HYBRID BEACON (ALSO KNOWN AS HAWK)

The pedestrian hybrid beacon (PHB) is located both on the roadside and on mast arms over the 

major approaches to an intersection. The head of the PHB consists of two red lenses above a 

single yellow lens. It is normally “dark,” 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 use to cross the major 

roadway. During the flashing pedestrian clearance interval, the PHB changes to a wig-wag 

flashing red to allow drivers to proceed after stopping if the pedestrian has cleared the roadway, 

thereby reducing vehicle delays. 

A recent study conducted a before-and-after evaluation of the safety performance of the PHB.

(108)

Using an empirical Bayes method, the study 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, the researchers counted the crashes that 

occurred during the study period, typically 3 years before and 3 years after the installation of the 

PHB. 

The researchers created two crash datasets. The first dataset included crashes coded as occurring 

at the intersecting streets (identified by using street names). The second dataset was a subset of 

the first dataset and only included those crashes that had “yes” for the intersection-related code 

in the police report.

The crash categories examined in the study included total, severe, and pedestrian crashes. From 

the evaluation that considered data for 21 pedestrian hybrid beacon treatment sites and 

102 unsignalized intersections (reference group), the researchers found the following changes in 

crashes after 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).

FHWA added the PHB to the MUTCD in the 2009 edition (see chapter 4F).(2) However, the 

PHBs included in the FHWA safety study differ from the material in the 2009 MUTCD in the 

following ways because the installations included in the FHWA study preceded the MUTCD 

guidance:
227

• Section 4F.02 of the MUTCD states the following(2):

When an engineering study finds that installation of a pedestrian hybrid 

beacon is justified, then ... the pedestrian hybrid beacon should be installed at 

least 100 feet [31 meters] from side streets or driveways that are controlled by 

STOP or YIELD signs.

All 21 pedestrian hybrid beacons included in this study are located either at a minor 

intersection (where the minor street is controlled by a stop sign) or at a major 

driveway (where the driveway is controlled by a stop sign). 

• The 2009 MUTCD depicts an R10-23 sign with the symbolic red circle and a white 

background for the word “crosswalk” on the sign.(2) The signs typically used at the 

PHB locations do not have the symbolic red circle, and the crosswalk background is 

yellow.

The MUTCD includes guidelines for the installation of the pedestrian hybrid beacons for lowspeed roadways where speeds are 35 mi/h or less, and high-speed roadways where speeds are 

more than 35 mi/h.

(2) Changes proposed for the next edition of the MUTCD (i.e., the version that 

will follow the 2009 edition) is to remove the 100-ft guidance statement and to add text stating 

that if the PHB is installed at or immediately adjacent to an intersection with a minor street, a 

stop sign shall be installed for each minor-street approach.

A study in Oregon investigated the public’s understanding of the PHB display.(109) A survey was 

conducted in Corvallis, which was selected because there were no PHBs installed there, and 

users would likely be seeing images of the device for the first time. Images of the PHB display

were shown to respondents, who were asked questions about what the various indications meant. 

Survey questions showed only a replication of the PHB display and consistently labeled the 

device a “signal.” Results of the survey indicated that the PHB was not widely recognized, 

especially when it was presented out of context, and there was confusion about the sequence of 

the six indications. The vast majority of respondents answered that they had not seen a PHB 

before or were not sure whether they had. Of the respondents that said they had seen a PHB 

before, a large majority (85 percent) responded that it was installed at a rail crossing. Many 

respondents did not understand the meaning of the various indications of the PHB. Both younger 

(67 percent) and older (49 percent) drivers responded correctly to the dark indication, stating that 

they knew to continue through the signal. Most (71 percent) responded that it was necessary to 

stop for the solid red indication, but there appeared to be confusion with the alternating flashing 

red indication. A low percentage of drivers correctly responded that they must stop but could 

proceed through if the crossing was clear. Researchers concluded that a public education 

campaign on the different indications of the device should precede the deployment of a new 

installation.
228

PUFFIN

A recent British report documented safety at pedestrian user-friendly intelligent (Puffin) 

crossings.

(110) Puffins have the following characteristics(56,110)

:

• Nearside pedestrian signals that encourage pedestrians to view oncoming traffic.

• No flashing pedestrian green period as at Pelican crossings (i.e., an acronym for

pedestrian light control; a British pedestrian crossing with traffic signals that are

controlled by pedestrians) or pedestrian signal blackout period at junctions (simplifies 

pedestrian signal phasing to “green man” for walk and “red man” for don’t walk and 

eliminates a flashing “don’t walk” for don’t start phase).

• On-crossing pedestrian detectors that provide an extension to the pedestrian clearance 

period while pedestrians are still within the crossing.

• No flashing amber traffic period as at Pelican crossings.

• An indicator light that confirms when the pedestrian signal has been activated.

• Pedestrian curbside detectors to cancel the pedestrian demand if there are no 

pedestrians in the wait areas.

Puffins were developed to replace Pelican crossings at midblock sites and far-side pedestrian 

signals at junctions. As reported by Maxwell et al., previous research has shown that, compared 

with existing pedestrian signal facilities, Puffin facilities can reduce both driver and pedestrian 

delay at junctions and improve pedestrian comfort (particularly for older pedestrians and those 

with impaired mobility).(110) The aim of the Maxwell et al. study was to quantify the safety

benefit. Accident data was analyzed from 50 sites (40 midblock crossings and 10 junctions) that 

had been converted to Puffin facilities from Pelican crossings and far-side pedestrian signals at 

junctions. The sites had no other significant changes in layout or operation, and were in general 

conformance with current Puffin guidance. Statistical analysis was undertaken by using a 

generalized linear model, which included time trends and seasonal factors. Midblock Puffin 

crossings were shown to be safer than Pelican crossings as follows:

• 17 percent lower at the midblock sites (statistically significant at the 5-percent level).

• 19 percent lower over all the sites (statistically significant at the 5-percent level).

• 24 percent lower for all pedestrian accidents (statistically significant at the 10-percent 

level).

• 16 percent lower for all vehicle accidents (statistically significant at the 10-percent 

level).
229

RAISED CROSSWALKS

Huang and Cynecki evaluated three raised crosswalks in Durham, NC, and Montgomery County, 

MD, using a treatment-and-control study approach.

(76) All three sites were on two-lane, two-way 

roadways. One site in Durham also had a continuously operating overhead flashing beacon in 

addition to the raised crosswalk treatment, and staged pedestrians were used at the Maryland site. 

The researchers found that speeds at the treatment sites were lower than at nearby control sites 

(table 195), but motorist yielding behavior was mixed (table 196).

Table 195. Comparison of vehicle speeds at raised crosswalks.

(76)

Location

50th percentile speed (mi/h) Significance at 

0.05 level or 

bettera Treatment site Control site

Difference in 

speeds

Durham, NC—Research Drive 20.7 24.7 4.0 Yes

Durham, NC—Towerview Drb 11.5 23.9 12.4 Yes

Montgomery County, MDc 21.5 24.0 2.5 No a

Significance based on two-tailed test. b

Towerview site had an overhead flashing beacon in addition to the raised crosswalk.

c

Speeds at Montgomery County site were measured only when the staged pedestrian was present.

Table 196. Pedestrians at raised crosswalks for whom motorists stopped.

(76)

Location

Treatment sitea

(percent (sample 

size))

Control sitea

(percent (sample 

size)) Significanceb

Durham, NC—Towerview Drive 79.2 (159) 31.4 (35) Y (0.000)

Montgomery County, MD 1.2 (169) 1.0 (198) N a

Sample size in parentheses. b

Y = Significant at the 0.10 level or better (p-value in parentheses); N = Not significant at the 0.10 level or better.

RECTANGULAR RAPID-FLASHING BEACON

See discussion in chapter 2 of this report. 

REFUGE ISLANDS

Crossing the street can be a complex task for pedestrians. Pedestrians must estimate vehicle 

speeds, adjust their walking speeds, determine adequacy of gaps, predict vehicle paths, and time 

their crossings appropriately. Drivers must see pedestrians, estimate vehicle and pedestrian 

speeds, determine the need for action, and react accordingly. At night, darkness and headlamp 

glare make the crossing task even more complex for both pedestrians and drivers.

(111) Some 

midblock crossings may be too wide to be crossed during available gaps without the protection 

of a signal. Median refuge islands simplify the street crossing task by permitting pedestrians to 

make vehicle gap judgments for one direction of traffic at a time. Recent refuge island designs 

can incorporate an angled or staggered pedestrian opening, which better aligns pedestrians to 

face the second direction of oncoming traffic. Refuge areas may be delineated by markings on 

the roadway or raised above the surface of the street.

Bowman and Vecellio in 1994 reported comparisons of several kinds of medians, including 

undivided multilane roadways, TWLTL, and raised curb medians.(54,112,113)Raised curb facilities 
230

were associated with lower pedestrian crash rates, but the authors reported that both raised and 

TWLTL medians significantly reduced the number and severity of vehicular crashes at the study 

sites. In general, raised curb medians may be better than TWLTL medians which are, in turn, 

better than undivided highways, but the literature search did not conclusively find that medians 

improved pedestrian safety.

(113)

A study by Bacquie et al. compared median refuge islands and split pedestrian crossovers in an 

analysis of crash reports at 10 crossing locations in Toronto, ON.

(114) The split pedestrian 

crossover treatment includes a median refuge island with pedestrian-activated signal control. The 

crash data were not normalized by exposure data, but some indication was given about 

pedestrian and vehicle exposure for the two treatments. The study found that pedestrians were 

seldom struck while standing on the refuge island and were more often struck while crossing,

due to poor gap judgment or improper driver yielding. Vehicle rear-end collisions were higher at 

the split pedestrian crossovers, and researchers surmised it was because it was a less common 

treatment than traditional intersection signals. The authors indicated some drivers did not act 

uniformly when approaching the split pedestrian crossovers because the drivers might not know 

when to stop or whether other drivers would stop in front or behind them.

Huang and Cynecki evaluated five refuge islands in Corvallis, OR, and Sacramento, CA, using a 

before-and-after study approach.

(76) The Corvallis site was on a four-lane urban arterial with a 

center left-turn lane, while the Sacramento sites were on intersections of two-way, two-lane 

residential streets. The authors reasoned that, because refuge islands constrict the roadway and 

slow vehicle speeds, the islands would increase the number of motorists yielding to pedestrians. 

In other words, more pedestrians would have the benefit of motorists yielding to them. However, 

none of the treatments had a statistically significant effect on motorist yielding, as shown in 

table 197.

Table 197. Pedestrians at refuge islands for whom motorists yielded.

(76)

Location Locations Lanes

Beforea

(percent 

(sample size))

Aftera (percent 

(sample size)) Significanceb

Corvallis, OR 1 4 + TWLTL 5.7 (35) 7.5 (53) Small

Sacramento, CA 4 2 32.6 (46) 42.1 (38) No a

Sample size in parentheses. b

No = Not significant at 0.10 level; Small = Small sample size.

Median refuge islands were installed at two signalized intersections in San Francisco and a 

midblock location in Las Vegas. Pécheux et al. reported that there were no measurable changes 

in the percentage of pedestrians trapped in the roadway, the percentage of pedestrians that were 

diverted to the crosswalk, or the percentage of pedestrian-vehicle conflicts at any of the sites 

where data for these MOEs were collected.

(4) They also found no significant impacts on drivers’ 

yielding behavior at the intersection locations, but yielding increased significantly at the 

midblock location, as shown in table 198. The researchers surmised that the installation of a 

median refuge island at a midblock location was effective in increasing driver yielding to 

pedestrians and reducing pedestrian delay, while the median refuge islands at the signalized 

intersections in San Francisco appeared to be less effective in altering driver and pedestrian 

behaviors.
231

Pécheux et al. also reported on an offset pedestrian opening in two other median islands.(4) The 

offset is a type of channelization that encourages pedestrians to turn and walk parallel to the 

traffic they are crossing; it provides refuge for pedestrians in terms of physical separation from 

traffic and ensures they are facing the traffic before crossing the second half of the roadway. The 

crosswalk was created using waist-high bollards and raised medians; the offset at the other study 

site was developed through median cutouts in an existing raised median, and a new marked 

crosswalk was added. At both locations the percentage of pedestrians trapped in the roadway fell 

significantly, particularly at the Lake Mead site with a 57 percent decrease (see table 199). 

Researchers suggested that the large percentage of pedestrians trapped at the Lake Mead site in 

the before condition was likely caused by the absence of a marked crosswalk. The research team 

also measured large, significant increases in driver yielding at both sites as shown in table 200.

Table 198. Drivers yielding to pedestrians at median refuge islands.

(4)

Location Site (location)

Percent of drivers yielding to 

pedestrians Percent

Before After change p-value

San Francisco

Geary & Stanyan 

(Intersection)

80.4

(n = 158)

86.6

(n = 164) +6.2 0.18

Geary & 6th (Intersection) 96.1

(n = 186)

89.7

(n = 262) -6.4 0.15

Las Vegas Harmon: Paradise Rd. to 

Tropicana Blvd. (Midblock)

22

(n = 77)

46

(n = 284) +24 < 0.001

Table 199. Trapped pedestrians at offset median openings.

(4)

Location Site

Percent of drivers yielding to 

pedestrians Percent

Before After change p-value

Las Vegas Maryland Pkwy & Dumont 12

(n = 631)

4

(n = 198) -8 < 0.001

Las Vegas Lake Mead: Belmont to 

McCarran

62

(n = 61)

5

(n = 123) -57 < 0.001

Table 200. Drivers yielding to pedestrians at offset median openings.

(4)

Location Site

Percent of drivers yielding to 

pedestrians Percent

Before After change p-value

Las Vegas Maryland Pkwy & Dumont 32

(n = 432)

76

(n = 246) +44 < 0.001

Las Vegas Lake Mead: Belmont to 

McCarran

3

(n = 296)

40

(n = 117) +37 < 0.001

ROAD DIETS

A road diet involves narrowing or eliminating travel lanes on a roadway to accommodate

pedestrians and bicyclists. While there can be more than four travel lanes before treatment, road 

diets are often conversions of four-lane, undivided roads into three lanes—two through lanes 

plus a center turn lane (see figure 104 and figure 105). The fourth lane may be converted to a 

bicycle lane, sidewalk, and/or on-street parking. Thus, the existing cross-section is reallocated. A 

recent HSIS report documented an empirical Bayes analysis of road diet installations in Iowa,
232

California, and Washington.

(115) Researchers estimated the change in total crashes resulting from 

the conversions in each of the two databases and combined these estimates into a crash 

modification factor (CMF). The empirical Bayes evaluation of total crash frequency indicated a

statistically significant effect of the road diet treatment in both datasets and when the results 

were combined. Table 201 shows the results from each of the two studies and the combined

results—the CMFs and their standard deviations.

Source: Pedestrian & Bicycle Information Center and FHWA.

Figure 104. Photo. Four-lane configuration before road diet.

(115,116)

Source: Pedestrian & Bicycle Information Center and FHWA.

Figure 105. Photo. Three-lane configuration after road diet.

(115,116)

Table 201. Results of EB analysis on four-lane to three-lane road diets.

(115)

State/site characteristics Accident type

Number of treated sites

(roadway length)

CMF

(standard 

deviation)

Iowa: Predominantly U.S. and State routes in 

small urban areas (average population of 

17,000)

Total crashes 15 (15 mi) 0.53 (0.02)

California/Washington: Predominantly 

corridors in suburban areas surrounding larger 

cities (average population of 269,000)

Total crashes 30 (25 mi) 0.81 (0.03)

All sites Total crashes 45 (40 mi) 0.71 (0.02)
233

SIDEWALKS

Tobey et al. investigated the safety effects of sidewalks.

(117) The researchers found that sites with 

no sidewalks or pathways were the most hazardous for pedestrians, with pedestrian hazard scores 

of +2.6. These scores indicate that crashes at sites without sidewalks are more than twice as 

likely to occur as expected. Sites with sidewalks on one side of the road had pedestrian hazard 

scores of +1.2, compared with scores of -1.2 for sites with sidewalks on both sides of the road. 

Thus, according to Tobey et al., sites with no sidewalks were the most hazardous to pedestrians, 

and sites where sidewalks were present on both sides of the road were least hazardous. 

Sidewalks separated from the roadway are the preferred accommodation for pedestrians.

(118)

Providing walkways for pedestrians dramatically increases how well pedestrians perceive their 

needs are being met along roadways. The wider the separation is between the pedestrian and the 

roadway, the more comfortable the pedestrian facility. One recent study indicated that roadways 

without sidewalks are more than twice as likely to have pedestrian crashes as sites with 

sidewalks on both sides of the street.

(118,119) By providing sidewalks on both sides of the street, 

numerous midblock crossing crashes can be eliminated.

ZIGZAG LINES

Zigzag lines are applied at midblock pedestrian crossings to restrict parking, stopping, and 

overtaking to improve pedestrian conspicuity. They are used in New Zealand, Canada, Europe, 

Trinidad, Great Britain, South Africa, Hong Kong, and Australia. A 2010 paper reviewed the 

literature to discuss how different countries use and interpret the meaning of zigzag pavement 

markings (lines) used at midblock crossings.

(120)

The review indicated that zigzag lines at 

pedestrian zebra crossings are misunderstood by most Trinidadian and Australian drivers as well 

as some researchers in North America. Such misunderstanding is associated with frequent 

vehicles parking, stopping, and overtaking in the vicinity of the pedestrian crossing. More 

education and public information on the crossing features and its use is needed. 

The Virginia Department of Transportation studied the effectiveness of zig-zag pavement 

markings (shown in figure 106) in Loudoun County where the Washington and Old Dominion

Trail crosses Belmont Ridge Road and Sterling Boulevard.

(121) Effectiveness was defined in three 

ways: 1) an increase in motorist awareness in advance of the crossing locations, 2) a positive 

change in motorist attitudes, and 3) motorist understanding of the markings. Motorist awareness 

was measured by computing the difference in vehicle speeds before and after the installation of 

the markings. Attitudinal changes were measured through a survey targeting motorists, 

pedestrians, and bicyclists familiar with the markings. Motorist understanding was measured 

through another survey administered elsewhere in the State that targeted motorists unfamiliar 

with the zig-zag markings in Loudoun County.

The study found that the zig-zag markings installed in advance of the two crossings heightened 

the awareness of approaching motorists. This was evidenced by reduced mean vehicle speeds 

within the marking zones; speed reductions were largely sustained at observations 6 and 

12 months after installation, compared with 1 week after installation. Further, the majority of 

survey respondents indicated an increase in awareness, a change in their driving behavior, and a 

higher tendency to yield than before. The study also found that motorists had limited 
234

understanding of the purpose of the markings. When seen with context, motorists’ correct 

interpretations of their meaning increased, but not to levels compatible with guidance set forth in 

the MUTCD.(2) Researchers concluded that public information and education campaigns would

help to increase understanding of the zig-zag pavement markings further.

Source: Dougald, Dittberner, and Sripathi/Transportation Research Record 2299.

Figure 106. Diagram. Schematic of zig-zag pavement marking design.

(121)

MULTIPLE TREATMENTS

A 2009 paper reported on the effectiveness of engineering countermeasures toward crash 

reductions at eight corridors in Miami-Dade, FL.

(121) A before-and-after study was used to 

compare the sequential implementation of a 3-year large-scale NHTSA project. The project

focused primarily on education and enforcement components followed by a large-scale FHWA 

engineering countermeasure project that was added to the NHTSA project along specific 

corridors. Results showed that the NHTSA pedestrian safety project reduced countywide 

pedestrian crash rates by 13 percent along the targeted corridors, and the FHWA engineering 

safety project produced a further reduction to 50 percent of the baseline level. These results 

translate to 50 fewer pedestrian crashes annually along the treated corridors. Countermeasures 

implemented included the following:

• Reduced minimum green time at midblock crosswalks controlled by a traffic signal.

• Advance yield markings at crosswalks with an uncontrolled approach.

• Recessed or offset stop lines for intersections with traffic signals.

• Leading pedestrian intervals.
235

• Pedestrian pushbuttons that confirm having been pressed.

• “Turning Vehicles Yield to Pedestrians” symbol signs for drivers.

• Elimination of permissive left turns at a signalized intersection.

• In-street pedestrian signs.

• Pedestrian zone signs.

• Midblock traffic signal.

• Intelligent transportation system (ITS) video pedestrian detection.

• RRFB for uncontrolled multilane crosswalks.

• ITS smart lighting at crosswalks with nighttime crashes.

• ITS “No Right Turn on Red” signs.

• Pedestrian countdown timers.

• Speed trailer.

In 2005, the Chicago Department of Transportation reported on the effects of a combination of 

traffic control devices and calming measures used to slow traffic and improve safety around 

schools.

(123) These measures included the following:

• Installation of speed humps along local street frontages of schools.

• Variable speed indicator signs giving interactive speed indication to motorists passing 

by schools on arterial streets.

• Installation of traditional school crossing warning signs and school zone 20 mi/h

speed limit signs.

• Experimental use of strong yellow/green pavement marking materials to mark 

crosswalks, “school” legends, speed humps, center lines, and stop bars in the blocks 

adjacent to schools.

The following summary was provided(123): 

The analysis conducted was limited by the absence of control locations where 

similar marking treatments might have been installed using standard white 

pavement marking colors for crosswalks, “SCHOOL” legends, stop bars, and 

speed hump markings. The program analysis also generally was limited to 

assessing the combined effect of yellow/green markings, improved signing, and 

speed humps (on local streets), rather than analyzing the effect of individual 

traffic control measures. Understandably, it was the City’s intent to maximize the 

impact on motorists to increase their awareness, slow traffic, and improve overall 

safety in the school zones, rather than simply conduct a limited experiment on 

alternating color pattern crosswalks using a combination of white and strong 

yellow/green pavement marking materials.

The usefulness of the crash analysis was somewhat limited by only having one 

year of After-condition data available for the 2002 Program installation locations. 

No After-condition analysis was possible for the 2003 Program locations, nor, 

obviously, for the 2004 Program schools. 
236

The results of the analysis suggest that the use of strong yellow/green pavement 

markings did not seem to have a significant effect on traffic speeds or crash 

experience. On arterial streets, the change in aggregate mean speeds, the 

aggregate percentage of traffic exceeding the speed limit, and the mode and 

median values of peak hour 85th percentile speeds was minimal. The use of speed 

indicators, which have proven effective in reducing speeds in other locations 

throughout the country, did not have a large effect on either speeds or crashes 

during school peak hours. The combined use of speed indicators and strong 

yellow/green markings also did not have a major impact on reducing speeds or 

crashes. 

On local streets, the locations studied all had a combination of speed humps and 

strong yellow/green pavement markings. Most of these locations already had allway stop control at adjoining intersections, thus already limiting the speeds on 

those streets. While the change in aggregate mean speeds and the aggregate 

percentage of traffic exceeding the speed limit was minimal, there did appear to 

be a reduction in the mode and median values of peak hour 85th percentile 

speeds. However, it seems reasonable to conclude that this reduction may have 

been largely attributable to the installation of speed humps rather than the

yellow/green markings or upgraded school zone signing. This conclusion was 

reflected by the perception of survey respondents on the relative effectiveness of 

speed humps versus yellow/green markings. (pp. 9–10)

The city of Los Angeles, CA, has developed what it refers to as a “Smart Pedestrian Warning” 

system that includes the following multiple pedestrian crossing treatments(67)

:

• Advance pavement messages (“PED XING”).

• Advance warning pedestrian signs.

• Extended red curb.

• Double posting of intersection pedestrian signs.

• Ladder-style crosswalk markings.

• Automated pedestrian detection (video imaging).

• Actuated alternating flashing overhead amber beacons.

This pedestrian crossing design and its various elements have evolved over the past several years 

based on experimentation and testing. To date, about 25 pedestrian crossing warning systems 

have been installed in Los Angeles. Fisher, in an undated paper, reports on informal evaluations 

by city engineering staff that indicate that this pedestrian warning system has improved motorist 

yielding to pedestrians from 20 to 30 percent to the 72- to 76-percent range.

(67) Their evaluation 

also indicates that, of the 24 to 28 percent of motorists who did not yield, at least they traveled

more slowly when approaching the enhanced crossings. For example, limited data indicate that 

85th percentile vehicle speeds were reduced from 2 to 12 mi/h.

A study by Chen, Chen, and Ewing sought to evaluate the relative effectiveness of five 

countermeasures in New York City—increasing the total cycle length, Barnes Dance, split-phase 

timing, signal installation, and high-visibility crosswalk—and examine potential trade-offs in 

their effectiveness in reducing pedestrian crashes and multiple-vehicle crashes.

(124) They adopted 
237

a rigorous two-stage design that first identified a comparison group of intersections, 

corresponding to each treatment group, and then estimated a negative binomial model with the 

Generalized Estimating Equation method to further control confounding factors and withinsubject correlation; the model also accounted for built environment characteristics. Researchers 

concluded that the four signal-related countermeasures were more effective in reducing crashes 

than high-visibility crosswalks, but they added that there are trade-offs between improving 

pedestrian safety and motorist safety. Treatments that indirectly resolve conflicts (e.g., increasing 

total cycle length and Barnes Dance) were more effective in reducing pedestrian crashes and yet 

less effective in reducing vehicle crashes than those that directly separate conflicts (i.e., split 

phase and signal installation). In the case of Barnes Dance, there was a potential increase in 

vehicle crashes. This finding suggests that selection of a specific countermeasure at a location 

highly depends on the characteristics of the location and the problem at hand. Researchers 

suggested that the types of conflicts and balance of time for different groups of road users at the 

intersections should be considered so that the improvement of the safety of one group does not 

compromise that of other groups.

239

ACKNOWLEDGMENTS

This research is sponsored by FHWA as part of the “Evaluation of Pedestrians Safety 

Engineering Countermeasures at Urban and Suburban Midblock Crossing Locations” project. 

The research was performed at the TTI and MRIGlobal. 

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, David Levinger, Ransford 

McCourt, Meghan Mitman, Richard Nassi, Richard Retting, and Roger Wentz. Their advice and 

guidance was greatly appreciated during the course of the study.

Several TTI employees assisted the authors with the closed-course study, including Mehdi 

Azimi, Jared Buckler, Seth Cole, Martin Fuest, Blanca Garcia, Jonathan Graber, Sarah 

Hammond, Michael Hardy, Laura Higgins, Grant Jackson, Greg Larson, Ivan Lorenz, Jeff Miles, 

Hailey Minter, Sarah Motes, Madison Munson, Scott Nemec, Jesse Ortega, Jose Rocha, 

Elizabeth Sandefur, Nada Trout, Dan Walker, Christine Yager, and Dick Zimmer. 

Several TTI employees also assisted the authors with the open-road study by collecting or 

reducing the data, including Adrian Contreras, Amanda Martin, Blanca Garcia, Carla Kolber, 

Colleen Dau, Dan Walker, Daniel Aaron, Jeff Miles, Jeremy Johnson, Michael Hardy, Mike 

Pratt, Nicholas Wood, Sarah Motes, and Seth Cole. John Ronchetto of MRIGlobal was also a 

key staff member in collecting open-road data.

The research team is also grateful to the representatives and staff of the cities that participated in 

and facilitated the open-road study: Austin, TX (Gary Schatz, Jim Dale, Ronnie Bell, and 

Willard Peavy); College Station, TX (Troy Rother and Lee Robinson); Flagstaff, AZ (Jeffrey 

Bauman, Randy Whitaker, and Steven Hill); and Milwaukee, WI (Robert Bryson and Scott 

Reinbacher).

We also value the comments provided by reviewers of draft versions of this material and 

members of the NCUTCD.

241

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