Information as a Source of Distraction 

PUBLICATION NO. FHWA-HRT-15-027 NOVEMBER 2015

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
FOREWORD

In the past, communicating messages legibly on electronic changeable message highway signs 

(CMS) was limited by older CMS technology to textual information, using just a few fonts. 

Today, CMS technology has advanced to the point where messages can display graphical 

information, including exact replicas of standard highway signs, and Federal Highway 

Administration fonts. Although what can be displayed on CMSs intended for highway 

applications is limited, the technology is capable of producing full color, animation, and video, 

and can display these images in bright daylight. This project examined the distraction potential 

of information sources in the right-of-way, with a focus on the latest generation of CMS 

technology that is capable of displaying any type of message—large or small letters, graphics,

and symbols. In one of the experiments, drivers did not look at the salient images (faces on 

brightly colored backgrounds) more often or longer than they looked at travel-time messages.

In addition to CMSs, the project evaluated the distraction potential of increasing the number of 

supplemental guide signs associated with a single interchange and potential information overload 

effects on drivers, from the frequency and spacing of guide signs. The results of that study 

supported retaining current Manual of Uniform Traffic Control Devices for Streets and Highways

standards and recommendations for guide signs and suggested further research on the design of 

specific-service logo signs. The research should be useful to engineers interested in standards 

for highway signing, to Transportation Management Center operators interested in conveying 

messages that complement driver behavior and to researchers interested in assessing driver visual 

attention.

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.

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-15-027

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

4. Title and Subtitle

Information as a Source of Distraction

5. Report Date

November 2015 

6. Performing Organization Code:

7. Author(s)

Vaughan W. Inman, Mary Ann Bertola, Brian H. Philips

8. Performing Organization Report No.

9. Performing Organization Name and Address

Leidos

6300 Georgetown Pike

McLean, VA 22101-2296

10. Work Unit No.

11. Contract or Grant No.

DTFH61-08-C-00006

12. Sponsoring Agency Name and Address

Manual of Uniform Traffic Control Devices Team

Federal Highway Administration

1200 New Jersey Ave. S.E.

Washington, DC 20590-9898

13. Type of Report and Period Covered

Final Report. 7/15/2011–9/30/2013

14. Sponsoring Agency Code

HOTO-1 

15. Supplementary Notes

The FHWA Contracting Officer’s Technical Representative was David Yang.

16. Abstract

The overall goal of the Information as a Source of Distraction project was to further the scientific basis for 

decisions about the types of information that can be displayed within the right-of-way without adversely affecting

drivers’ attention to their primary task—safe driving. There were two focus areas: electronic changeable message 

highway signs (CMS) and guide signs. Six studies were conducted. The first study examined the perceived 

similarity between messages on a full-color, full-matrix, light-emitting diode CMS display with 0.79-inch

(20-mm) pixel pitch and the same messages on a liquid crystal display. The purpose of that study was to derive 

requirements for laboratory and driving simulation studies of CMS messaging. The second study examined the 

legibility distance for text message on the CMS display used in the first study. It was determined that, assuming

20/40 vision, legibility distance could be estimated using a letter height of 1 inch (2.54 cm) per 20 ft (6.1 m) of 

viewing distance. In the third study, drivers read the CMS display as they approached it on a closed course that 

required them to simultaneously navigate a curved path. The effects of CMS message properties such as flashing, 

phasing, abbreviations, and use of symbols versus text were examined. The fourth and fifth studies simulated 

overhead CMS messages on a freeway on which there was a CMS every 0.5 mi (0.8 km). By displaying highly 

salient images (faces on brightly colored backgrounds) that changed every 3 s, an attempt was made to distract 

drivers. Drivers did not look at the salient images more often or longer than they looked at travel-time messages. 

When headways were short, the salient signs had a 0.2 probability of receiving a brief look. None of the signs 

caused drivers to miss safety-critical messages encountered later in the drive. None of the signs caused drivers to 

fail to detect a roadway hazard (spilled logs). The final study examined the effects of the frequency and spacing 

of guide signs on navigation performance and eye-glance behavior. That study supported retaining current 

Manual of Uniform Traffic Control Devices for Streets and Highways standards and recommendations for guide 

signs and suggested further research on the design of specific service logo signs.

17. Key Words

Distraction, Changeable message sign, CMS,

Supplemental guide sign, Specific service sign, 

Sign spacing, Sign frequency, Sign content

18. Distribution Statement

No restrictions. This document is available 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

137

22. Price

N/A

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 o

F 

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

EXECUTIVE SUMMARY .......................................................................................................... 1

CHAPTER SUMMARY........................................................................................................... 1

SUMMARY OF FINDINGS AND RECOMMENDATIONS .............................................. 3

CHAPTER 1. INTRODUCTION TO THE INFORMATION AS A SOURCE OF 

DISTRACTION PROJECT................................................................................................... 5

OBJECTIVES........................................................................................................................... 5

BACKGROUND ....................................................................................................................... 5

Operational Definition of Distraction ..................................................................................... 6

Measuring Attention and Distraction...................................................................................... 7

New CMS Capabilities ........................................................................................................... 9

Current CMS Guidelines and Knowledge Gaps................................................................... 10

CMS Properties..................................................................................................................... 12

Contrast................................................................................................................................. 14

Supplemental Guide Signs.................................................................................................... 17

APPROACH............................................................................................................................ 19

CHAPTER 2. LABORATORY EVALUATION OF CMS..................................................... 21

INTRODUCTION .................................................................................................................. 21

METHOD ................................................................................................................................ 21

Materials ............................................................................................................................... 21

Procedure .............................................................................................................................. 22

Participants............................................................................................................................ 23

Stimuli................................................................................................................................... 23

RESULTS ................................................................................................................................ 23

Exit Closed (Side-by-Side) ................................................................................................... 23

Exit Closed (LCD Close, CMS Far) ..................................................................................... 24

End (Side-by-Side)................................................................................................................ 24

End (LCD Close, CMS Far).................................................................................................. 25

High Wind (Side-by-Side) .................................................................................................... 25

High Wind (LCD Close, CMS Far) ...................................................................................... 26

Accident Ahead Merge Left (White) (Side-by-Side)............................................................ 27

Accident Ahead Merge Left (Orange) .................................................................................. 27

No Peds................................................................................................................................. 28

Exit 47 B, Route 7 N, Closed................................................................................................ 28

Shoulder Work ...................................................................................................................... 29

DISCUSSION.......................................................................................................................... 29

CHAPTER 3. LEGIBILITY TESTING................................................................................... 31

METHODS.............................................................................................................................. 31

Equipment............................................................................................................................. 31

Procedure .............................................................................................................................. 31

Participants............................................................................................................................ 33

RESULTS ................................................................................................................................ 33

DISCUSSION.......................................................................................................................... 34
iv

CHAPTER 4. CMS FIELD TEST............................................................................................. 35

INTRODUCTION .................................................................................................................. 35

METHODS.............................................................................................................................. 35

Participants............................................................................................................................ 35

Test Facility .......................................................................................................................... 35

Changeable Message Sign .................................................................................................... 35

Research Vehicle................................................................................................................... 37

Message Types...................................................................................................................... 37

Test Procedures..................................................................................................................... 42

RESULTS ................................................................................................................................ 42

Message Length .................................................................................................................... 44

Flashing................................................................................................................................. 45

Symbols Versus Text ............................................................................................................ 46

Abbreviations........................................................................................................................ 47

Phasing or Paging ................................................................................................................. 47

Case....................................................................................................................................... 48

DISCUSSION AND CONCLUSIONS.................................................................................. 48

Message Length .................................................................................................................... 49

Flashing................................................................................................................................. 49

Symbols Versus Text ............................................................................................................ 49

Abbreviations........................................................................................................................ 49

Phasing or Paging ................................................................................................................. 50

Case....................................................................................................................................... 50

CHAPTER 5. THE EFFECT OF REPEATED IRRELEVANT CMS MESSAGING ON 

THE DETECTION OF SAFETY-CRITICAL MESSAGING......................................... 51

INTRODUCTION .................................................................................................................. 51

METHODS.............................................................................................................................. 51

The Simulator........................................................................................................................ 51

The Simulation Scenario....................................................................................................... 52

Eye-Tracking System............................................................................................................ 56

Participants............................................................................................................................ 57

RESULTS ................................................................................................................................ 57

Response to the Incident-Related Detour Message............................................................... 57

Gaze Behavior....................................................................................................................... 58

Relationship Between Exit Taking and Gaze Behavior........................................................ 60

Glance Results ...................................................................................................................... 60

Look Results ......................................................................................................................... 62

Fixation Results .................................................................................................................... 65

Driving Performance Measures ............................................................................................ 67

DISCUSSION.......................................................................................................................... 67

Respect for CMS as TCDs.................................................................................................... 67

Gaze Behavior....................................................................................................................... 68
v

CHAPTER 6. THE EFFECT OF CMS INFORMATION ON DETECTION OF 

SAFETY-CRITICAL EVENTS IN THE ROADWAY..................................................... 71

INTRODUCTION .................................................................................................................. 71

METHOD ................................................................................................................................ 71

The Simulation...................................................................................................................... 72

Recognition Test ................................................................................................................... 73

Participants............................................................................................................................ 73

RESULTS ................................................................................................................................ 73

Response to the Encounter With a Spilled Load .................................................................. 73

Gaze Behavior....................................................................................................................... 74

DISCUSSION.......................................................................................................................... 83

Distraction From Monitoring the Road Ahead ..................................................................... 83

Eye Gaze Distraction Evidence............................................................................................. 84

Recognition of Faces............................................................................................................. 84

SUMMARY AND CONCLUSIONS ..................................................................................... 84

CHAPTER 7. THE EFFECT OF FREQUENCY AND SPACING OF GUIDE SIGNS 

ON DRIVER BEHAVIOR................................................................................................... 87

METHODS.............................................................................................................................. 89

The Simulator........................................................................................................................ 89

The Simulation Scenario....................................................................................................... 91

Eye-Tracking System............................................................................................................ 94

Participants............................................................................................................................ 96

Instructions............................................................................................................................ 96

RESULTS ................................................................................................................................ 97

Exit Taking............................................................................................................................ 97

Gaze Behavior..................................................................................................................... 100

Driving Performance Measures .......................................................................................... 116

DISCUSSION........................................................................................................................ 118

SUMMARY AND CONCLUSIONS ................................................................................... 119

REFERENCES.......................................................................................................................... 121
vi

LIST OF FIGURES

Figure 1. Formula. Contrast ratio...................................................................................................14

Figure 2. Photo. A series of four consecutive supplemental guide signs at a Virginia 

interchange...............................................................................................................................18

Figure 3. Photo. Message advising that exit is closed. ..................................................................23

Figure 4. Photo. Exit closed sign example (proportionally scaled and zoomed in).......................24

Figure 5. Photo. Version A of the end sign....................................................................................24

Figure 6. Photo. Version B of the end sign....................................................................................25

Figure 7. Photo. Solid version end sign comparison stimulus.......................................................25

Figure 8. Photo. Emulation of high wind warning sign.................................................................26

Figure 9. Photo. Version B of the high wind comparison stimuli. ................................................26

Figure 10. Photo. Accident ahead merge left sign with white pixels that yielded a mean 

similarity rating of 5.6 between the CMS and LCD displays..................................................27

Figure 11. Photo. Accident ahead merge left sign with grey pixels that yielded a mean 

similarity rating of 4.7 between the CMS and LCD displays..................................................27

Figure 12. Photo. Accident ahead merge left sign with solid white letters that yielded a mean 

similarity rating of 5.6 between the CMS and the LCD displays. ...........................................27

Figure 13. Photo. Accident ahead merge left example. .................................................................28

Figure 14. Photo. No peds message on LCD with emulated CMS pixels. ....................................28

Figure 15. Photo. No peds message on LCD with filled-in space between emulated CMS 

pixels........................................................................................................................................28

Figure 16. Photo. Exit 47 B message on LCD with emulated CMS pixels. ..................................29

Figure 17. Photo. Exit 47 B message on LCD with filled-in space between emulated CMS 

pixels........................................................................................................................................29

Figure 18. Photo. Shoulder work CMS message emulation on LCD. ...........................................29

Figure 19. Photo. Sign used for legibility testing. .........................................................................31

Figure 20. Graph. Legibility testing results. ..................................................................................33

Figure 21. Photo. Layout of course on drag strip. .........................................................................36

Figure 22. Photo. The CMS used in the study. ..............................................................................36

Figure 23. Photo. Example of a three-word message. ...................................................................38

Figure 24. Screen capture. Two-degree ROI for the CMS at a distance of approximately 

1,250 ft (381 m). ......................................................................................................................43

Figure 25. Screen capture. Two-degree ROI for the CMS approximately halfway down the 

slalom course. ..........................................................................................................................43

Figure 26. Screen capture. Two-degree ROI for the CMS toward the end of a run. .....................43

Figure 27. Graph. Expected mean trial duration as a function of the number of words in the 

CMS message...........................................................................................................................45

Figure 28. Graph. Expected mean response duration as a function of the number of words in 

the CMS message.....................................................................................................................45

Figure 29. Chart. Trial duration and response duration as a function of flashing mode. ..............46

Figure 30. Chart. Comparison of trial durations for workers symbol signs with other text and 

symbol signs.............................................................................................................................47

Figure 31. Chart. Expected mean number of looks at CMSs as a function of the number of 

message phases. .......................................................................................................................48

Figure 32. Photo. FHWA highway driving simulator....................................................................52
vii

Figure 33. Photo. Travel time to McLean was displayed once per mile. ......................................53

Figure 34. Photo. Faces serving as salient but driving-irrelevant information..............................53

Figure 35. Photo. Accident ahead message. ..................................................................................54

Figure 36. Photo. ROI indicated on CMS......................................................................................57

Figure 37. Chart. Expected mean distance from CMS at the last glance.......................................62

Figure 38. Chart. Expected probability and 95-percent confidence limits of at least one look 

at a CMS as a function of sign content (blank or not blank) and mean headway....................63

Figure 39. Chart. Estimated mean number of looks and 95-percent confidence limits as a 

function of sign content and time headway. ............................................................................64

Figure 40. Chart. Expected mean look duration as a function of sign content and time 

headway. ..................................................................................................................................65

Figure 41. Chart. Expected mean and 95-percent confidence limits for fixation duration as a 

function of data collection zone and headway.........................................................................66

Figure 42. Screen capture. Driver’s view of spilled load from 128 ft (39 m). ..............................72

Figure 43. Chart. Predicted probability and confidence limits for a glance at a CMS as a 

function of sign content. ..........................................................................................................75

Figure 44. Chart. Expected mean duration of glances at CMSs and confidence limits as a 

function of sign content. ..........................................................................................................76

Figure 45. Chart. Expected glance-end distance as a function of sign content. ............................76

Figure 46. Chart. Probability of at least one look at a CMS as a function of type of sign 

content......................................................................................................................................77

Figure 47. Chart. Predicted number of looks at a CMS as a function of type of sign content. .....78

Figure 48. Chart. Number of glances at each CMS that received at least one look. .....................79

Figure 49. Chart. Predicted mean duration of individual looks as a function of sign content.......79

Figure 50. Chart. Expected mean probability of a fixation on a CMS as a function of sign 

content......................................................................................................................................80

Figure 51. Chart. Predicted mean number of fixations across all signs as a function of sign 

content......................................................................................................................................80

Figure 52. Chart. Expected mean fixation duration as a function of sign content.........................81

Figure 53. Chart. Expected mean distance of fixations on a CMS as a function of sign 

content......................................................................................................................................82

Figure 54. Chart. Expected mean speed as a function of sign content. .........................................83

Figure 55. Photo. View of the three LCD monitors from slightly behind the driver’s eye 

point. ........................................................................................................................................90

Figure 56. Screen capture. Analyst’s screen for coding shows ROI for specific-service signs 

that are 400 ft (122 m) apart. ...................................................................................................95

Figure 57. Screen capture. ROI drawn around a specific-service sign..........................................95

Figure 58. Screen capture. ROI drawn around an advance guide sign. .........................................96
viii

LIST OF TABLES

Table 1. Measured CIE color coordinates for colors displayed on the CMS and the RGB 

settings used to generate those colors......................................................................................22

Table 2. Measured CIE color coordinates for colors displayed on the LED/LCD and the 

RGB settings used to generate those colors.............................................................................22

Table 3. RGB values used in comparisons with message advising that exit is closed ..................24

Table 4. RGB values and similarity ratings of four versions of the high-wind warning sign 

with the CMS standard.............................................................................................................26

Table 5. RGB values and rating results for the close high wind comparison stimuli....................26

Table 6. RGB values for the four orange alternatives to the accident ahead merge left 

standard....................................................................................................................................28

Table 7. Ratings for negative contrast orange shoulder work sign................................................29

Table 8. Luminance and CIE color coordinate measures for colors of fonts used in legibility

testing.......................................................................................................................................32

Table 9. Observed legibility and linear transform of legibility distances for persons with 

20/40 visual acuity ...................................................................................................................34

Table 10. Signs used for comparisons of symbol- and text-sign reading time and glance 

behavior....................................................................................................................................39

Table 11. Vocabulary for abbreviations test..................................................................................39

Table 12. Eight two-phase messages used for phasing trials.........................................................40

Table 13. Comparison of static message length to phased message length...................................41

Table 14. Messages used for comparison of recognition performance between all uppercase 

messages and messages in pseudo uppercase/lowercase .........................................................41

Table 15. Number of looks to messages and look duration as a function of the number of 

words in the messages..............................................................................................................44

Table 16. Expected mean number looks to messages in the flashing text trials............................46

Table 17. Desired speed cumulative probability distribution of the simulated traffic stream.......55

Table 18. Desired speed cumulative probability distribution at congestion locations ..................55

Table 19. Response to warning to take next exit by drivers for whom eye-tracking data were 

available ...................................................................................................................................58

Table 20. Response to warning to take next exit by all drivers who completed the drive ............58

Table 21. Relationship between glance to the accident ahead message and taking exit ...............60

Table 22. Mean counts and mean durations for various gaze metrics to the accident ahead 

message ....................................................................................................................................60

Table 23. Distribution of fixation durations ..................................................................................66

Table 24. Number of fixations longer than 2 s as a function of time headway and sign 

content type..............................................................................................................................67

Table 25. Number of participants who hit or avoided spilled load shown as a function of 

load location and CMS content................................................................................................73

Table 26. Distribution of fixation durations. .................................................................................81

Table 27. Frequency of fixations greater than 2 s..........................................................................82

Table 28. Summary of the seven signing conditions.....................................................................91

Table 29. Vehicles/lane/h summary statistics for each data collection zone.................................93

Table 30. Speed parameters (percent vehicles per speed bin per lane) for traffic control by 

the traffic simulation model.....................................................................................................93
ix

Table 31. Percent of participants on segments 1–7 who responded correctly to guide signs 

(no/no and yes/yes) and false alarms (no/yes) and misses (yes/no) as a function of 

signing condition......................................................................................................................98

Table 32. Percent of participants on segments 8–14 who responded correctly to the 

supplemental guide signs (no/no and yes/yes) and false alarms (no/yes) and misses 

(yes/no) as a function of signing condition..............................................................................99

Table 33. Percent of participants on segments 15–21 who responded correctly to the 

specific-service signs (no/no and yes/yes) and false alarms (no/yes) and misses (yes/no) 

as a function of signing condition............................................................................................99

Table 34. Percent of participants who responded in either conditions 6 or 7 or both 6 and 7.....100

Table 35. Probability of glance at signs as a function of condition and sign type.......................101

Table 36. Probability of glancing at a subsequent guide sign given a glance at the preceding 

advance guide sign.................................................................................................................102

Table 37. Mean glance duration in seconds where 0 s was used for no glance...........................103

Table 38. Mean glance duration in seconds for sign ROIs given a glance occurred...................104

Table 39. Comparison of the GEE estimates proportion of gaze time between conditions 1 

and 2 for the entire data collection zone ................................................................................105

Table 40. Comparison of the GEE estimates of the proportion of gaze between conditions 1 

and 2 for the area around the first advance guide sign ..........................................................106

Table 41. Comparison of the GEE estimates proportion of gaze between conditions 2 and 3 

for the entire data collection zone..........................................................................................106

Table 42. Comparison of the GEE estimates of the proportion of gaze between conditions 2 

and 3 for the area around the first advance guide sign ..........................................................106

Table 43. Comparison of GEE estimates of the proportion of gaze between conditions 2 and 

4 for the entire data collection zone.......................................................................................107

Table 44. Comparison of GEE estimates of the proportion of gaze between conditions 3 and 

5 for the entire data collection zone.......................................................................................107

Table 45. Comparison of GEE estimates of the proportion of gaze between conditions 3 and 

5 for the partial data collection zone......................................................................................108

Table 46. Comparison of GEE estimates of the proportion of gaze between conditions 5 and 

6 for the entire data collection zone.......................................................................................108

Table 47. Comparison of the GEE estimates of the proportion of gaze between conditions 5 

and 6 for the partial data collection zone ...............................................................................109

Table 48. Comparison of GEE estimates of the proportion of gaze between conditions 6 and 

7 for the partial data collection zone......................................................................................109

Table 49. Summary of findings for proportion of gaze to signs..................................................110

Table 50. Probability of fixating on each ROI category as a function of condition....................111

Table 51. Mean number of fixations as a function of condition and ROI category ....................112

Table 52. Mean fixation duration on ROIs given at least one fixation........................................113

Table 53. Estimated mean proportion of fixations on signs, road ahead, and other as a 

function of signing condition over the entire data collection zone........................................114

Table 54. Estimated mean proportion of fixations on ROI categories as a function of signing 

conditions 2 and 3 over the entire data collection zone.........................................................114

Table 55. Estimated mean proportion of fixations on ROI categories as a function of signing 

conditions 2 and 4 ..................................................................................................................114
x

Table 56. Estimated mean proportion of fixations on ROI categories as a function of signing 

conditions 3 and 5 ..................................................................................................................115

Table 57. Estimated mean proportion of fixations on ROI categories as a function of signing 

conditions 5 and 6 ..................................................................................................................115

Table 58. Estimated mean proportion of fixations to the ROI categories as a function of 

signing conditions 6 and 7 .....................................................................................................116

Table 59. Partial data collection zone estimates of the mean proportion of fixations on ROI 

categories as a function of signing conditions 6 and 7 ..........................................................116

Table 60. Planned between-condition vehicle speed comparisons..............................................117

Table 61. Vehicle estimated mean speed (in mi/h) in the partial data collection zones as a 

function of signing condition.................................................................................................117
xi

LIST OF ABBREVIATIONS

AASHTO American Association of State Highway Transportation Officials

CIE Commission Internationale de l'Eclairage (International Commission on 

Illumination

CMS Electronic Changeable Message Highway Sign

FHWA Federal Highway Administration

GEE Generalize Estimating Equation

ITS Intelligent Transportation System

LCD Liquid Crystal Display

LED Light-Emitting Diode

M Mean

MUTCD Manual on Uniform Traffic Control Devices for Streets and Highways

NCHRP National Cooperative Highway Research Program

NEMA National Electrical Manufacturers Association

NTCIP National Transportation Communications for Intelligent Transportation 

System Protocol

PRC Percent of Road Center

RGB Red, Blue, Green

ROI Region of Interest

SE Standard Error of the Mean

SDG Standard Deviation of Gaze Angle

TCD Traffic Control Device

1

EXECUTIVE SUMMARY

The overall goal of the Information as a Source of Distraction project was to further the scientific 

basis for decisions about the types of information that can be safely displayed within the right-ofway without adversely affecting drivers’ attention to their primary task—safe driving. There 

were two focus areas: electronic changeable message highway signs (CMSs) and guide signs.

This study had the following objectives:

• Determine the distraction potential of non-traffic-related messages. 

• Determine the distraction potential of guide signs that are more closely spaced or more 

frequent than current guidelines permit. 

• Perform both on-road and driving simulator evaluations of distraction.

• Create a report and present recommendations that provide a scientific basis for 

practitioners to assess the informational load imposed on road users by information 

sources within the highway right-of-way.

CHAPTER SUMMARY

Chapter 1, the report’s introduction, discusses the definition of distraction, the properties of 

modern light-emitting diode (LED) based CMSs, and the specific issues addressed in the 

subsequent chapters.

Chapter 2 presents some laboratory assessments of a full-color, full-matrix, LED display with 

0.79-inch (20-mm) pixels. The display represents capabilities of the current generation of CMSs

that are compliant with intelligent transportation system (ITS) standards. In one experiment,

observers rated the visual similarity of messages on the CMS with the same messages displayed 

on a 60-inch (152-cm) liquid crystal display. The results of that experiment suggest that it is not 

necessary to emulate individual pixels of the CMS display to generate images that observers rate 

as reasonably similar to the CMS images. The results will have significance for future CMS 

research in either the Federal Highway Administration’s (FHWA) sign laboratory or the FHWA 

highway driving simulator. 

Chapter 3 presents a study of the legibility of the CMS described in chapter 2. These results 

suggest that the legibility distance of the 0.79-inch (20-mm) pixel-pitch full-color display used in 

this test provides a longer legibility distance than the previous generation of CMS displays that 

used a 1.6-inch (40-mm) pixel pitch with amber LEDs.(1) They also suggest that for the display 

type used and drivers with approximately 20/20 vision, the 90- to 100-percentile legibility 

distance can be estimated using a factor of 40 to 45 ft/inch (4.8 to 5.4 m/cm) of letter height. This 

equates to a somewhat lower legibility distance for 0.79-inch (20-mm) pixel pitch CMS display

than that provided by the 30 ft/inch (3.6 m/cm) legibility distance criterion in the Manual on 

Uniform Traffic Control Devices for Streets and Highways (MUTCD), assuming a 20/40 visual 

acuity.

(2) (Because the relationship between letter height and visual acuity is linear, the MUTCD 
2

criterion would yield a 60-ft/inch (7.2-m/cm) legibility distance for individuals with 

20/20 visual acuity.)

Chapter 4 examines the legibility of CMS messages in a dynamic roadway environment. In this 

study, observers drove toward the CMS on a winding path at 25 mi/h (40 km/h) and read the 

messages on the sign as soon as possible. An eye-tracker was used to assess when the observers 

were looking at the signs. The time and distance for the beginning and ending of response were 

recorded. The following properties of the messages on the sign were assessed for their effect on 

gaze behavior and message reading:

• Message length. The amount of time required to read messages of up to five words was 

fairly constant. Six- and seven-word lists took considerably longer to read. It is 

recommended that text messages be limited to five or fewer words whenever possible.

• Flashing. The MUTCD standard is not to flash messages.(2) When the first line of a 

message was flashed, the duration of observers’ responses was longer than for either 

static messages or all-lines-flashing messages. The delay before participants began 

responding was longest for all-lines-flashing messages. The findings support the current 

MUTCD standard.

• Symbols versus text. The symbol versions of messages (e.g., an electronic version of the 

signal ahead symbol sign) that filled the same display area as their text equivalents were 

legible or recognizable from a greater distance than their equivalents. The exception to 

this finding was the road workers ahead symbol sign, which was unfamiliar to the 

majority of observers. It is recommended that a familiar symbol be used in place of a text 

message when: (1) the high-resolution CMS can accurate portray the symbol, and (2) the 

symbol is recognizable at the same or greater distance as the equivalent text message.

• Abbreviations. It was found that use of the abbreviations approved in the MUTCD had 

no adverse effects compared with the spelled out versions of the abbreviated words.(2)

Chapter 5 presents an experiment conducted in the FHWA highway driving simulator to evaluate 

whether frequently changing overhead freeway CMS displays with human faces and colorful 

backgrounds would distract drivers more than static travel-time-related messages or a blank 

CMS. Another purpose of the experiment was to evaluate whether noncritical information 

presented on frequently occurring CMSs (i.e., a CMS every 0.5 mi (.8 km)) would cause drivers 

on a 48-min trip to lose respect for or habituate to the CMS messaging and result in drivers 

missing a safety-critical CMS message. The results showed that gaze behavior (i.e., number of 

looks, duration of looks) did not differ between signs with rapidly changing faces and static text 

messages. Drivers briefly looked at signs with changing faces and travel times about 40 percent 

of the time (40 percent of the signs) when headway to the car ahead was greater than 1.5 s. When 

headways were shorter than 1.5 s, the probability of briefly looking at a non-blank sign was 

about 0.15. Regardless of what was displayed on the CMS, the mean duration of individual looks 

was 0.2 s or less.

For the simulated trip, in which the drivers passed under 96 noncritical CMS message signs, 

there was no indication of habituation. The 97th CMS carried the message “ACCIDENT 
3

AHEAD ALL LANES BLOCKED USE NEXT EXIT.” Of the 32 participants, 9 failed to exit 

after passing under that sign. However, only one of the nine participants claimed to be unaware 

of the critical message. The majority of the remaining participants tried to exit but were unable to 

find a safe gap to change lanes.

Chapter 6 presents another experiment in the FHWA highway driving simulator similar to the 

experiment described in chapter 5. However, in this experiment, a spilled load of logs was in the 

participant’s lane, and the primary dependent measure was whether the participant avoided 

hitting the spilled load. There were 72 CMSs—1 every 0.5 mi (0.8 km), the 3 sign types 

(changing faces, travel-time messages, and blank) occurred in cycles of 3. The 72d sign was 

blank for a third of the participants, had a travel-time message for another third, and had 

changing faces for the final third. The spilled load came early in the trip (just before the third 

CMS) for half the participants and late in the trip (just before the last CMS) for the other half. Of 

80 participants, 21 hit the logs. There was no significant relationship between the content of the 

CMS at the spill site and the probability of hitting the spilled load. 

Chapter 7 examined the effect of the frequency and spacing of guide signs on navigation, eye 

glance behavior, and driving performance. The primary focus was the frequency and spacing of 

specific-service signs and supplemental guide signs. The number of supplemental guide signs 

varied between zero and three as did the number of specific service signs. The number of 

destinations on guide signs varied between one and two. The distance between the three types of 

guide signs also varied. In most conditions, the spacing minimum was 800 ft (244 m), which is 

the current standard. In the remaining two conditions, the spacing was 400 and 200 ft (122 and 

61 m). Overall, the results support the current MUTCD standard of 800 ft (244 m) spacing 

between signs and up to two destinations on advance and supplemental guide signs. There was 

some evidence that the specific-service six-panel logo signs used in this study required too much 

visual attention, especially when there was more than one specific-service sign. Participants 

appeared to scan food and gas service signs in search of a lodging destination and thus did not 

appear to use the sign legends in their search strategy.

SUMMARY OF FINDINGS AND RECOMMENDATIONS

The overall goal of the project was to further the scientific basis for decisions about the types of 

information that can be displayed within the right-of-way without adversely affecting drivers’ 

attention to their primary task—safe driving. There were two focus areas: electronic CMSs and 

guide signs. Findings and recommendations include the following:

• Research on messaging can be done on laboratory and driving simulator displays without 

precisely emulating the pixel spacing or color properties of CMS devices. It is sufficient 

to emulate legibility distance and approximate color and contrast.

• The daytime legibility distance of 0.79-inch (20-mm), full-color, full-matrix CMS text 

can be estimated using a letter height of 1 inch (2.4 cm) per 20 ft (6.09 m). 

• Large symbols that motorists easily recognize have greater legibility distance and require 

less visual attention than equivalent word messages that require the same display area.
4

• CMS messages should be limited to five words or fewer whenever possible, especially if 

the messages contain rarely used elements (e.g., evacuation routing or atypical lane 

closures).

• Visually salient CMS images (faces) that change every 3 s attracted no more attention 

than static travel-time text messages. Blank signs attract less attention than populated 

signs.

• Drivers attend to CMSs when driving demands are manageable.

• The frequently occurring and driving irrelevant CMS messaging used in this study did

not appear to cause drivers to rapidly habituate to or lose respect for CMSs such that 

safety-critical messaging would be ignored. This study did not examine whether longterm habituation might occur over many trips. It is possible that messaging that cannot be 

easily recognized as driving irrelevant (e.g., messages that must be fully read to 

determine irrelevance) might result in loss of respect if encountered frequently.

• CMS messages that were theoretically highly visually salient did not distract drivers from 

detecting roadway hazards. It appears that drivers have learned to regulate their visual 

attention in a way that minimizes susceptibility to visual distractions. This finding does 

not imply that drivers cannot be distracted by CMS messages but rather that visual 

salience alone is not sufficient to distract.

• The MUTCD criteria for the frequency and spacing of guide signs, including 

supplemental and specific-service signs, were supported by the current findings.(2)

• Further research is recommended to address why participants in this study searched fueland food-specific service signs when their task was to search for a specific lodging 

business.
5

CHAPTER 1. INTRODUCTION TO THE INFORMATION AS A SOURCE OF 

DISTRACTION PROJECT

The overall goal of the Information as a Source of Distraction project was to further the scientific 

basis for decisions about the types of information that can be displayed within the right-of-way

without adversely affecting drivers’ attention to their primary task—safe driving. The goal was

to be accomplished by evaluating the distraction potential of various types of information when 

displayed on electronic changeable message highway signs (CMSs), especially non-trafficrelated messages displayed within the right-of-way. In addition to CMS messaging, the

distraction potential of frequent and closely spaced freeway navigation guide signs was to be

considered. 

OBJECTIVES

The objectives of this project were to determine the distraction potential of various types of 

information when displayed within the right-of-way. In particular, the studies were to determine 

the potential of CMS depictions of road signs and non-traffic-related messages and other 

information sources and to determine the distraction potential of guide signs that are more 

closely spaced or more frequent than current criteria permit. The objectives were to be achieved 

through performance of both on-road and driving simulator evaluations. The researchers were to 

create a report and present recommendations with a scientific basis to enable practitioners to 

assess the informational load imposed on road users by information sources within the highway 

right-of-way.

BACKGROUND

CMSs are used by highway agencies to communicate current information to drivers.1

Information displayed on these signs may include travel times, incident information, temporary 

lane restrictions, and alternate route suggestions. CMSs are intended to aid motorists, 

transportation agencies, and the general public.

(2) The signs are thought to benefit motorists by 

increasing safety and reducing travel delays. Transportation agencies are thought to benefit from 

improvements in network operational efficiencies. The general public is thought to benefit from 

reductions in pollution that result from network efficiencies.

Because CMS display technology continues to evolve and improve, the potential for the 

development of novel CMS uses is likely to continue. It is also conceivable that some of the 

extensive guidance for CMS operations and policy may become obsolete as technology 

continues to evolve.(3–5) The FHWA has requested additional research to assist future decision 

making, and guide validation or update of CMS standards and guidelines contained in the 

Manual on Uniform Traffic Control Devices for Streets and Highways (MUTCD).

(2) The present

project is intended to serve that role, with an emphasis on guidance to avoid unnecessary driver 

distraction by CMS content. This literature review is intended to summarize the current

knowledge base regarding CMS information display and its relation to driver distraction.

 

 1Changeable message signs are also referred to as variable message signs and dynamic message signs. This report 

uses CMS throughout.
6

Distraction is a construct that is related to attention and stimuli that inappropriately divert or 

attract attention. Before distraction can be studied in a scientific manner, an operational 

definition is required.

Operational Definition of Distraction

In their book on driver distraction, Regan, Lee, and Young define distraction as “...a diversion of 

attention away from activities critical for safe driving toward a competing activity.”(6) They go 

on to say the following:

...[D]riving is a complex, multitask activity, making it likely that the demands of 

one element of driving will interfere with another element. Considering driving as 

a single activity in defining distraction oversimplifies a complex activity and 

neglects important driving-related distractions that drivers must manage. (p. 35) 

Because driving involves the management of multiple tasks, such as monitoring the road ahead, 

monitoring the speedometer, monitoring the review mirrors, and perhaps reading safety-critical 

messages on a CMS, Regan et al. qualify distractions as an “inappropriate” distribution of 

attention.(6)

The US-EU Bilateral ITS Technical Force working group on driver distraction provided a similar 

definition of driver distraction to that provided by Regan, Lee, and Young.

(7) The members of the 

working group also distinguished between appropriate and inappropriate attention. They 

acknowledge a rear-end collision with a suddenly braking vehicle would not be caused by 

distraction if the driver of the following vehicle was performing a shoulder check while merging 

with mainline freeway traffic. That is, when two safety-critical demands for attention are 

simultaneous, workload—not distraction—would be the main causative factor for a collision. 

The working group defined critical for safe driving tasks as those that allow the driver to avoid 

or not cause a crash. The working group acknowledged that safety-critical tasks may be 

situation dependent and liable to interpretation. They caution against using hindsight (i.e., 

whether a crash actually occurred) as a criterion for determining safe driving. As yet, however, 

there are no universally accepted metrics for assessing safe driving in the absence of a crash. 

Horberry and Edquist discuss distraction from elements outside of the vehicle.(8) Their analysis 

focuses on billboards and the potential for billboards to attract driver attention from the built 

roadway, which they define as roadway geometry, the roadway surface, and traffic signs and 

markings. Despite this narrow scope, they conclude that although it seems intuitively obvious 

that billboards are a distraction to drivers, there is little research to support or refute this view. 

Their analysis provides little guidance on an approach to the distraction potential for CMSs that 

are part of the built roadway. However, their analysis does provide suggestions for the direction 

of future research. They identify the following three types of distracting stimuli:

(8)

• Visual distraction of attention capture.

• Cognitive distraction.

• Secondary activity.
7

Visual distraction occurs when a strong visual stimulus causes an involuntary glance, as might 

occur in response to a bright flash of light. Cognitive distraction occurs when a stimulus causes 

the driver to inappropriately devote mental attention to a matter not immediately necessary to 

safe driving, such as engaging in a conversation. Secondary activities are non-driving-related 

activities in which drivers voluntarily engage, such as searching for a vehicle identified in an 

AMBER alert that was posted on a CMS.

Thus, in the study of the relationship between CMS content and driver distraction, it is suggested 

that researchers are looking at the content of CMS that results in inappropriate diversion of 

attention away from activity critical for safe driving to CMS stimuli that do any of the following:

• Capture drivers’ attention involuntarily.

• Capture drivers’ visual or mental attention for longer than is appropriate.

• Prompt additional activities that inappropriately draw resources away from safe-drivingrelated tasks, e.g., tuning the radio or writing down the license number from an AMBER 

alert while negotiating a lane change in traffic.

There are several challenges to successfully addressing the information distraction issue. CMSs

on freeways are primarily, if not exclusively, traffic control devices (TCDs). Thus by definition,

CMSs are part of the roadway, and reading them is part of the driving task. In some cases, CMSs

contain information critical to safe driving. This implies that a CMS, when used as a TCD, can 

only be distracting if it causes an inappropriate diversion of attention (i.e., when the information 

on the CMS is not more critical to safe driving than other roadway visual stimuli). When used to 

display non-traffic-related messages, e.g., advertising, AMBER alerts, or public service 

announcements (e.g., click it or ticket), the bar for distraction is lower (i.e., this information is 

not safety critical) and any attention that would otherwise be devoted to safety critical tasks 

would then constitute distraction. Even in the case of non-traffic-related messages however, there 

is room for diversion of driver attention, if that diversion does not detract from safety-critical 

activity. 

The amount of driver mental capacity available for safety-critical driving tasks can vary from 

time to time, person to person, and place to place. For instance, required capacity can vary in 

time with the level of traffic. Persons who are fatigued may have less available capacity.(8)

Freeway sections with sharp curves, narrow lanes, or frequent weaving generally require more 

attention than straight, wide, uninterrupted sections. Therefore, assessing the distraction effects 

of CMS information requires either methods of measuring attention requirements and available 

driver attention capacity or the use of some putative surrogate measure of distraction.

Measuring Attention and Distraction

Young, Regan, and Lee discuss various driving performance measures to assess the distraction 

effects.(9) They mention lateral (lane) tracking performance, longitudinal (following headway) 

performance, gap acceptance, steering entropy, reaction time, and speed maintenance as potential 

driving performance measures that have been shown to vary with putative levels of distraction. 

They caution that these measures are not interchangeable. 
8

Visual distractions (tasks that require looking away from the roadway) have been shown to affect 

lateral tracking performance, whereas tasks with moderate levels of cognitive distraction are 

associated with improved lateral control (measured by lane tracking and steering entropy). 

Reaction times to traffic events have been shown to be degraded by cognitive distraction. Young, 

Regan, and Lee also reported that in many cases driving simulations are more sensitive to driving 

performance degradation than are on road and test track assessments.(9) However, they caution 

that this greater sensitivity in driving simulations may be the result of shifts in drivers’ criterion 

for attending to the driving and distraction tasks (i.e., in simulators drivers may give higher 

priority to attending to the distraction and lower priority in attending to the driving task). How 

this shift in priorities relates to driving safety in the real world is unknown. However, this 

phenomenon points to the importance of measuring both primary (driving) and secondary 

(cognitive) task performance to assess how drivers are trading off performance in the two tasks.

Where drivers look can also be a clue to where their attention is directed.(10) Although it is 

possible to attend to locations on which the eyes are not focused and the level of attention to the 

location where the gaze is fixed can vary because of cognitive distraction, the center of gaze is 

generally a good indication of where drivers are attending.(11,12) Furthermore, there is a long 

history of using eye-tracking technology in the study of how drivers attend to traffic signs.

(13,14)

The fovea is the area of the retina that has the highest resolution and is used for reading normal 

text. The fovea covers about 2 degrees of visual angle. To capture fine detail, the direction of 

gaze must change to place the detail within the fovea. Assuming good contrast, an individual 

with 20/20 visual acuity can read a highway sign with letters 18 inches (46 cm) high from 

1,000 ft (305 m) away. At 1,000 ft (305 m), a 2-degree cone of vision would encompass a 

diameter of 36 ft (11 m). A CMS of 8 ft (2.4 m) in height (typical of a gantry-mounted overhead 

CMS on freeways) with the bottom of the sign at 20 ft (6.1 m) above the roadway would fall 

within this 2-degree cone, even if the driver is focused on a vehicle on the roadway below the 

sign. Furthermore, when deployed in a moving vehicle, well-calibrated eye trackers are limited 

to about 1.5 degrees of resolution.

(15) As a result, a field study with an eye tracker may not be 

able to establish with certainty when a driver first begins reading an overhead freeway sign or 

when the first glance at the sign occurs. At 500 ft (152 m), the 2-degree cone of fine vision has 

an 18-ft (5.2-m) diameter, so at that distance, it should be possible to determine with reasonable 

certainty whether a driver’s eyes are directed toward an overhead sign or to the roadway ahead. 

However, at 500 ft (152 m), another complication arises. The 18-inch (46-cm) letter will subtend 

10 min of arc or about twice the angle required for legibility. Text of this size, especially if it is 

expected, such as a route number or speed limit, would be legible in near peripheral vision (2 to 

10 degrees from the center gaze). This means that a driver may comprehend a CMS message 

without the necessity for the direction of gaze falling on the text. Three information unit text 

messages (what, where, and action) suggested for CMS messages by Dudek are probably too 

complex to be read quickly with near-peripheral vision.(3,5) However, the new technology CMSs

(see the next section, New CMS Capabilities) are capable of displaying symbols that constitute a 

single item of information and might be recognizable in near-peripheral vision. Also, drivers 

might be able to detect single high-priority words, such as “ACCIDENT,” in near-peripheral 

vision.

The use of percent of time gazing at the forward roadway (i.e., percent road center (PRC)) has 

been proposed as an inverse measure of driver visual distraction to in-vehicle devices.(16) PRC 
9

would typically not be appropriate for assessment of distraction caused by CMS content because 

at least portions of the signs can be read without taking the driver’s gaze away from the forward 

roadway, which these authors defined as a 16-degree cone about the forward path. As noted in 

the previous paragraph, a cone that large would include obstacles on the road and an overhead 

CMS for at least 500 ft (152 m) of travel distance.

The standard deviation of gaze angle (SDG) (computed as the sum of the square root of x

2 plus 

y

2

, where x is the difference between the previous horizontal gaze angle and the current gaze 

angle, and y is the analogous difference in the vertical plane) is reported to be sensitive to 

cognitive workload (mental distraction not caused by a competing visual task).(12) Therefore,

SDG might be an appropriate indicator of distraction by CMS content if that content increases 

driver cognitive distraction. Victor et al. found that cognitive distraction decreased the standard 

deviation in gaze angle compared with a no distraction baseline.(16) However, they could not 

show that SDG varied with the other levels of cognitive distraction they employed (i.e., levels of 

distraction other than the no distraction baseline). They speculated that the easiest cognitive 

distraction task maximized workload so that more difficult tasks could not show additional 

decrements. The distracter task they used was to count the number of times a target tone 

occurred within a string of 15 tones. The difficulty was manipulated by varying the number of

different target frequencies (two, three, or four) for which the participant had to listen.

Lane tracking, whether measured by steering entropy or lane deviations, would also not be a 

suitable measure of attention devoted to CMS content for the following reasons: (1) although 

lane tracking has been found to be sensitive to visual distraction by in-vehicle devices, gazes at

CMSs would still allow monitoring of lane position with near peripheral vision, and (2) lane 

tracking has not been found to be sensitive to degradation from cognitive distraction.(12)

Reaction time to traffic events has been shown to be degraded by cognitive distraction as defined 

by increased mental workload. Thus, to the extent that CMS content is suspected of increasing 

mental effort, reaction time to events in the roadway might be used to test the presence of this 

distraction effect.

Time headway appears to be a weak predictor of distraction.

(17) In an extensive series of on-road 

and driving study tests, there was some suggestion that visual distraction (attention to an invehicle device) results in slower speeds and longer headways, whereas cognitive distraction 

results in somewhat shorter headway in some studies, but not others.

New CMS Capabilities

Currently, CMS vendors are marketing ITS grade (National Transportation Communications for 

ITS Protocol (NTCIP) and National Electrical Manufacturers Association (NEMA) compliant) 

light-emitting diode (LED) CMSs for freeways with pixels as small as 0.47 inches (12 mm). 

Each pixel of a full-color CMS is made up of red, green, and blue LEDs. Such signs can display 

color images with symbols that closely match the MUTCD color specifications.(18) Although the 

ITS grade signs cannot display full-motion video or rapidly refreshed animations, commercial 

LED signs (often supplied by the same vendors that market ITS signs) can.
10

Current CMS Guidelines and Knowledge Gaps

This section provides an overview of the current state of knowledge concerning the human 

factors aspects of CMS content.

The following distraction effects are discussed in this section:

• Attraction. CMS stimuli that capture attention involuntarily.

• Information overload. CMS stimuli that capture drivers’ visual or mental attention for 

longer than is appropriate.

• Unintended driver reactions. CMS stimuli that prompt secondary activities that 

inappropriately draw resources away from safe-driving-related tasks.

Attraction

The idea that distraction can be the result of an involuntary capture of attention is related to the 

orienting response in the classical conditioning paradigm. A bright light that suddenly and 

unexpectedly illuminates may cause an orienting response—a response in which the individual 

involuntarily shifts attention and gaze to the stimulus. Whether a CMS could cause an orienting 

response is questionable. Orienting responses are easily extinguished.(19) That is, if exposure to 

the stimulus is repeated and the stimulus is not related to current priority goals—in this case 

driving—then the orienting response to the stimulus will stop occurring. Thus, even if the sudden 

onset of a CMS image or message would cause an orienting response on first occurrence, drivers 

would soon learn not to orient to such changes unless those changes reliably predicted an event 

that has priority over watching the road and monitoring the vehicle state. 

Information Overload

The construct of information overload was aptly described in National Cooperative Highway 

Research Program (NCHRP) Report 488:

(20)

“Driver information overload” results from providing too much information, 

through devices or conditions, for a driver to perceive and respond properly. 

Therefore, the information load on a driver is a property not only of the specific 

sign they are encountering, but also of the roadway context in which the sign 

occurs, the information context in which the sign occurs, the behavior 

characteristics of the driver, and the particular navigational task. Where drivers 

are confronted with more information than they can process, they may decelerate 

severely or drive unduly slowly, make late or erratic maneuvers, take an improper 

route alternative, ignore critical information, fail to monitor other traffic, or have 

excessive eyes-off-the-road episodes. These behaviors have obvious safety and 

operational consequences. (Foreword)

Information overload might occur if a driver perceives the need for the information on a TCD but 

extraction of the information requires more attention resources than the driver can devote to the 

message without risking degradation of driving performance.
11

Unintended Driver Reactions

Information overload may be possible when drivers either have to gaze at a CMS too long, or 

drivers have to devote too much attention (mental effort) to extracting meaning from a CMS. 

Two studies that appear to provide direct evidence of the distracting effect of information 

overload from CMS messages are cited here.

Erke, Sageberg, and Hagman conducted a study of driver responses to route diversion messages 

of CMS in Oslo, Norway.(21) At one site, the messages displayed indicated that a tunnel ahead 

was closed and suggested a ring road diversion. In fact, the tunnel was not closed. They found an 

increase of about 25 percent in the proportion of drivers exiting at the suggested ring road. 

Virtually all traffic left the freeway before reaching the tunnel. Thus, it appeared that the 

messages were nearly 100-percent effective in communicating that the tunnel was closed. 

However, there were unintended consequences. Mean speed was reduced in the proximity of the 

sign by about 5 mi/h (8 km/h). There was also a significant increase in speed variance and a 

significant increase in the number of brake light illuminations within reading distance of the 

sign. Video recordings suggested that part of the decrease in speed and braking events resulted 

when the lead vehicle in a platoon braked thereby requiring braking by closely following 

vehicles. Unfortunately, the authors did not measure legibility distance nor do they report the 

size of the text on the signs, which used amber LEDs. The text messages on the signs consisted 

of four lines but only three units of information (location, event, and action). The authors imply 

that the signs were legible from about 500 ft (150 m). At the posted speed of 50 mi/h (80 km/h),

drivers would have had about 5.5 s to read the sign, or slightly less than the 2 s per information 

unit suggested by Dudek.(3) The Erke et al. study demonstrates that CMS can adversely affect 

driving performance and potentially affect safety. However, because the signs in this study 

appear to fall short in legibility distance and perhaps because the messages had more lines than

are typical, it should not be concluded that these effects would be observed with messages that 

fully comply with the guidance provided by Dudek.

A similar slowing for four-line CMSs was reported for a simulator study that primarily focused 

on the effect of bilingual CMS messages on driver performance.(22) In that study, drivers slowed 

for four-line message signs, whether monolingual or bilingual, but did not slow for one- or 

two-line messages. A significant reduction in time headway was also observed only for four-line 

message signs when the vehicle ahead varied its speed on approach to the signs. For bilingual 

four-line messages, the investigators tried to mitigate the performance changes by placing a 

blank line between the English and Welsh messages (two lines each). This mitigation strategy 

had no effect. However, the number of trials in which the blank line was present was small, and 

participants were not told ahead of time what the line indicated (English above, Welsh below). 

Thus, in locations where bilingual CMSs may be desirable in the United States (e.g., States that 

border Mexico and Quebec), strategies such as visual separation of message lines might still be 

effective if drivers know what the visual separation means and also have more than minimal 

exposure to the visual separation. Because Welsh, English, Spanish, and French use nearly 

identical alphabets, drivers need to process all the lines of a bilingual message to extract the part 

of the message that is meaningful to them. It is reported that when the characters are distinctly 

different (e.g., Japanese and English or Greek and English), four-line bilingual signs do not 

hinder driver performance.(22)
12

The studies described in this section suggest that speed and headway changes in the vicinity of a 

CMS are potentially useful driving performance measures for assessing the distraction effect of 

CMSs because erratic control of speed and headway could indicate a diminution of attention to 

the driving task. Note that if a driver slows to allow more time to read a CMS, the headway of 

the driver who slows will increase, but that will cause the headway of following drivers to 

decrease.

CMS Properties

This section reviews some of the CMS properties that, depending on implementation and

operational variations, may be related to the human factors that may result in reduced or 

increased driver distraction.

Pixel Size (Dot Pitch)

Currently, NTCIP- and NEMA-compliant, full-matrix, full-color CMSs for freeway 

implementations have pixel sizes that range from 0.79 to 2.36 inches (20 to 60 mm).

(23,24) Each 

pixel consists of red, green, and blue LEDs. In some cases, the two red LEDs are used in 

combination with one blue and one green. Although the pixels, even those of 0.79 inches (2 cm)

in diameter, would seem to be large, at the viewing distances of 100 to 1,000 ft (30.5 to 

300.5 m), the individual pixels can appear to form fairly smooth continuous lines and curves. 

Font Issues

Previous research on the legibility of text on CMSs does not necessarily apply to CMSs with 

0.79- or 1.33-inch (20- or 34-mm) CMS displays. Early research found that the number of matrix 

elements did not affect legibility distance.(25) Although Garvey and Mace tested a font that 

consisted of 0.79-inch (20-mm) pixels, they did not attempt to replicate FHWA fonts, which can 

be approximated with 0.79-inch (20-mm) pixels and 18-inch (46-cm) letters. They also did not 

test uppercase/lowercase lettering with 0.79-inch (20-mm) pixels. 

Much of Europe is in the process of adopting the TERN font for CMS.(26) The TERN font is used 

on conventional European traffic signs, so in that sense, it is comparable to the FHWA 

standard fonts.

Garvey, Pietrucha, and Meeker reported that recognition distance (i.e., the distance at which 

destination names could be distinguished from each other) was greater when uppercase/

lowercase lettering (i.e., first letter capitalized and the remaining letters in lowercase) than all 

uppercase lettering.(27) Their stimuli were all positive-contrast white letters on a green (guide 

sign) background. Garvey et al. suggest that the uppercase/lowercase benefit derives from 

participants’ ability to distinguish word shapes from the configuration of risers and descenders in 

uppercase/lowercase text. In their study, participants were given a destination name to look for,

and the participants responded by indicating whether the target destination was on the first, 

second, or third line of a sign. With no expectation regarding what words to anticipate, shape 

was of less benefit. Indeed, the investigators found that legibility distance did not vary between 

uppercase/lowercase and all uppercase destination names when the participants had no 

expectation of what destinations would be shown on the sign. Apparently on the basis of this 

finding, the 2009 MUTCD was amended to specify street name and destination names be in 
13

uppercase/lowercase font. Other text on guide signs is to remain all upper case.(2) This study’s 

research team was unable to identify research that examines whether case has an effect on 

recognition or legibility distance on high-resolution CMS displays. At least for positive contrast, 

similar effects may be true for CMSs. In tests with negative contrast text, Holick, Chrysler, and 

Park found no advantage for uppercase/lowercase text over upper case test.(28) However, this 

seemingly contrary finding may be because participants in the study had no expectation 

regarding the message content. For CMSs with a limited vocabulary and where location names 

are used, uppercase/lowercase text may provide a recognition distance benefit. This hypothesis is 

testable.

Symbols

McDougall, Tyrer, and Folkard reported search times for icons are faster when the icons possess

a quality they refer to as “objectness.”(29) Quickly recognized icons are not necessarily the least 

complex (although complexity may affect legibility). Thus the I-10 route shield may be reacted 

to more quickly than the I-10 text if the text is treated perceptually as two objects and the route 

shield as one. This prediction is easily testable. 

Icons that have good legibility distance, that are recognized in context by most drivers, and that 

are familiar are likely to speed sign comprehension. The only study this research could identify 

that appears to confirm this conjecture was difficult to evaluate. Wang, Collyer, and Clark 

reported that recognition of CMS messages with icons was slightly faster than text-only 

messages.(30) However, the generalizability of their findings is doubtful. Participants had to 

decide which of two messages was being displayed. In this situation, recognition of either the 

text or the icon was sufficient to support a response. Icons were used to communicate incident 

type (e.g., crash, congestion, and slippery wet pavement), and text provided the location and 

recommended action. Only the icon or first line of all text messages needed to be recognized. 

Had participants needed to read the entire text or icon plus text message, the results may have 

been different. Also, there were hints in the data of speed/accuracy tradeoffs. Although the 

correlation between speed and accuracy was not significant, accuracy was not analyzed as a 

dependent measure. Thus, the possibility that the use of familiar icons or symbols could speed 

sign comprehension or increase legibility distance has not been fully and unambiguously 

evaluated.

Motion

Motion or animation and flashing are not allowed by the MUTCD. Lane closure portable arrow 

boards, which are included in the MUTCD, use a sequence of chevrons to imply motion;

however, these boards do not create true apparent motion and do not qualify as animation (see 

section 6F.61 of the MUTCD).

(2) These boards are currently only approved for temporary lane 

closures in conjunction with other TCDs such as channelizing devices and static signs. With the 

advent of active traffic management, which includes lane closures using permanent overhead 

CMS, the evaluation of effectiveness of similar sequenced symbols seems warranted.

Luminance

The MUTCD does not specify the required luminance for CMSs other than dictating that it meets

industry standards for day and night-time illumination.(2) The current industry standard is NEMA 

TS 4.(23) The MUTCD specifies that the luminance automatically adjust to ambient light and 
14

recommends that the contrast ratio between lit and unlit portions of the sign remains between 

8 and 12 (see section 2L.03, paragraphs 10 and 11 of the MUTCD).

(2)

Contrast

As indicated in the previous paragraph, it is recommended that the contrast ratio between lit and 

unlit elements of CMSs be between 8 and 12. 

The contrast ratio C is defined by the formula shown in figure 1.

Figure 1. Formula. Contrast ratio.

Where:

Lon = luminance of an area of the display with pixels on.

Loff = luminance of the same area with pixels off.

Sun position is an important element of CMS legibility. When the Sun is shining from behind the 

driver onto the face of the sign, contrast can be considerably reduced. The amount of reduction 

depends on the maintenance of the sign and on the design of the sign face. NEMA TS 4 specifies 

the test procedure for verifying standard conformance when the Sun is 10 degrees above a 

perpendicular to the sign face.(23) When the Sun is shining from behind the sign into the driver’s 

face, apparent contrast will depend on the amount of shielding around the sign. Standards for 

shielding the sign or illuminating sign elements in this adverse lighting condition were not 

identified.

Messaging

The most complete guide to CMS message construction is probably the guide prepared for the 

New Jersey Department of Transportation.(5) That document provides guidance on message 

structure, vocabulary, length, flashing, and paging of messages. Although it provides thorough 

and apparently sound advice for CMS operators, the empirical foundations for that advice are not 

cited. These foundations may be found in earlier publications, many of which are cited in the 

following sections.

Flashing

Currently, the MUTCD does not allow flashing any part of a message on a CMS (see section 

2L.05, paragraph 5).

(2) Numerous studies have looked at the effects of flashing messages on 

CMS. Flashing is generally intended to increase the conspicuity or attention capturing of 

important CMS messages.(31) However, few evaluations examine the effects of flashing on 

attention capture. One study that did look at the effect of flashing on conspicuity was reported by 

Charlton.(32) In that study, the effects on conspicuity were mixed. A flashing school warning sign 

increased search conspicuity and memory for that warning compared with a static school 

warning sign. A flashing road work ahead sign also proved more conspicuous than a static road 

work ahead sign of the same size, but was not more conspicuous or memorable than an oversized 

static version of the same sign. Charlton concluded that the effectiveness of flashing for 

attracting attention and improving memory for CMS messages may vary with the message. 

C = (Lon – Loff)/Loff
15

However, the Charlton study looked at only a small subset of CMS symbols and no text 

messages. The overall effectiveness of flashing as a conspicuity enhancement deserves further 

research.

A number of studies have shown that flashing may make CMS text messages harder to read. 

These studies examined reading time and comprehension for flashing messages and did not 

weigh the benefits of faster reading time or better comprehension against the potential for more 

drivers to attend to the sign or the likelihood that the sign would affect driver behavior in the 

desired manner.

(33–35) One study in which messages were presented on a laptop computer found 

that reading times were increased by about 20 percent if any part of a message was flashed.(34)

This finding seems reasonable because the amount of time the message is exposed is decreased 

when it flashes. Nonetheless, the Dudek 2006 study failed to replicate the reading time effect in a 

driving simulator.(33) Comprehension was not affected when an entire three-line message was 

flashed.

(34) However, recall of the third line of simulated CMS messages was depressed when 

only the first line of the message flashed. 

No research was identified that looks specifically at whether flashing on a CMS distracts drivers 

in a manner that would adversely affect safety. Dudek et al. did not find effects on driving 

performance in a simulator as a result of flashing.(34) In that study, the roadway was straight, and 

drivers had to monitor the forward roadway to maintain a safe following distance behind a 

simulated vehicle that braked unpredictably.

Flashing is used by some agencies to increase the conspicuity and apparent importance of CMS 

messages. There is a dearth of research showing that flashing accomplishes its intended purpose 

and little research that shows negative effects on reading time or comprehension that would 

outweigh the putative conspicuity benefits. Although the current body of research does not 

support elimination of the ban on flashing, neither is there evidence that either operational 

effectiveness or safety is adversely affected by the ban. Research in this area might clarify these 

issues. 

Vocabulary

The appropriate vocabulary and syntax for text-based messages was one of the earliest areas of 

human factors CMS research and analysis. (See references 36 through 40.) More recently, 

research has focused on methods of presenting travel times on CMS.(41,42) The research in this 

area seems adequate to meet user needs for permanent full-matrix color CMSs.

Message Length

CMS technology (e.g., the number of characters that can be displayed) and the amount of time 

CMS messages are legible to drivers constrain the amount of information that can be displayed. 

When approaching drivers are traveling at freeway speeds, CMS text with 18-inch (46-cm) 

characters is legible for a maximum of about 8 s. The MUTCD specifies 18 inches (46 cm) as the 

minimum letter height for CMSs on freeways (see section 2L.04, paragraph 6 of the MUTCD).

(2)

Because CMS legibility distance has been found not to increase much beyond that afforded by an 

18-inch (46-cm) letter height, this height has tended to become the standard height. Obstructions,

such as other vehicles, horizontal of vertical curves, and adverse atmospheric conditions (e.g., 

rain), can further reduce the amount of time that drivers are exposed to legible messages. 
16

Furthermore, early research in driver glance behavior showed that drivers do not look at signs in 

single long glances.(13) Rather, drivers make multiple short glances interspersed with glances to 

the roadway and other vehicles. Thus, for a static sign that is legible for 8 s, the total time a 

driver may glance to areas where the sign text can be read will sum to only about 2 s on average 

but may be as much as 6 s.(13) Dudek has estimated that each “unit” of information on a CMS can 

be read in about 2 s.(3) He recommends that CMS messages be limited to three or at most four

units of information. Dudek defines a unit of information as the answer to a simple question. For 

CMS these questions are usually what (e.g., crash), where (e.g., ahead), and what action should 

be taken (e.g., use frontage road).

In the section on unintended driver responses, this report noted that some studies with messages 

that probably violate the Dudek guidance on message length resulted in drivers slowing or 

braking. There is also some evidence that at least some drivers slow in response to CMS 

messaging even when the messages are properly formatted.

(43) It is unlikely that improvements in 

CMS technology could justify text-only messages longer than those currently recommended. 

Abbreviations

No research was identified that showed that abbreviations that are understood by more than 

85 percent of drivers take longer to comprehend than the complete words that would be used in 

their stead. Huchingson and Dudek and Hustad and Dudek reported many of the abbreviations 

specific to portable CMSs are almost universally understood when shown with a prompt—a 

word with which the abbreviation would always appear (e.g., New Jersey TRNPK).(44,45) To date,

the study of highway sign abbreviations has only considered comprehension. The effect of 

abbreviations on reading and comprehension time and effort should be considered in addition to 

simple comprehension measures because the time available for drivers to view CMS messages 

can be short.

Phasing

Of all the currently allowed approaches to displaying messages on CMSs, phasing is probably 

the most problematic in terms of the potential for driver distraction. Phasing, or paging, breaks a 

message into two or more parts, with each part displayed in sequence on the same sign. Phasing 

is used to overcome space limitations. The MUTCD limits phasing to two parts, with each part 

having no more than three lines (see section 2L.05, paragraph 4).

(2) The minimum duration of a 

phase is 1 s per word or 2 s per information unit. The maximum cycle time is 8 s, which would 

mean that at freeway speeds, a driver would be exposed to only one cycle during the time that 

the message is legible. The MUTCD also requires that the intelligibility of the message not 

depend on the order in which the two phases are read. 

The distraction potential of phasing is high because drivers typically make multiple short glances 

to signs rather than single long glances. The first glance could come at any time during either 

phase. If more than one glance is required to extract the information from a phase, there is no 

guarantee that the same phase will be present when the driver returns for a second glance. If the 

second glance arrives during a new phase, the driver will need to reorient attention to the new 

message phase. Once oriented, a third glance may be necessary to begin processing the second 

phase. A fourth glance might be needed to complete processing of the second phase, but this 

glance could potentially land not on the second but on the first phase, which would again call for 
17

a reorientation of attention. All this would be done while the driver continues to monitor the 

roadway, so that timing of glances to the sign would depend on what the surrounding traffic is 

doing. Furthermore, the MUTCD allows for a blank interval between phases of up to 0.3 s. 

Research has suggested that readability, measured by recall, is improved by the insertion of a 

0.3 s blank interval between phases. A blank phase may prevent masking of one message by 

another, or it may just allow drivers time to prepare to attend to a new phase, e.g., shift attention 

to the upper left portion of the display. However, a driver’s glance might arrive during a blank 

interval, which could either cause a driver to linger on the display until a message appears or 

return attention to the road and delay reading the sign. In any case, phasing increases the effort 

and time required to read a CMS and thus will increase the probability that attention will be on 

the sign when a safety-critical event occurs.

No previous studies were identified that examined the effects of CMS phasing on driver behavior 

in the field or employed eye tracking to quantify how drivers manage CMS messaging with the 

other visual requirements of the driving task. The effects of phasing on reading time are reported 

in chapter 4 of this report. 

Supplemental Guide Signs

The MUTCD describes the use of supplemental guide signs (see figure 2) as follows:

(2)

Supplemental Guide signs can be used to provide information regarding 

destinations accessible from an interchange, other than places displayed on the 

standard interchange signing. However, such Supplemental Guide signing can 

reduce the effectiveness of other more important guide signing because of the 

possibility of overloading the road user’s capacity to receive visual messages and 

make appropriate decisions. (Section 2E.35)
18

Figure 2. Photo. A series of four consecutive supplemental guide signs at a Virginia 

interchange.

Supplemental guide signs are approved for guiding unfamiliar travelers to certain types of 

destinations. Destinations specifically approved for supplemental signing are major colleges and 

universities, large military bases, major event facilities (e.g., sports arenas and stadiums), State 

and national parks, monuments, and major recreational areas.

(46) For each of these destination 

types, the American Association of State Highway and Transportation Officials (AASHTO) has 

provided specific minimum traffic generation criteria. Among the destinations for which 

supplemental guide signs are deemed inappropriate are businesses, churches, government 

research centers, driver’s license centers, any schools not qualified as major colleges or 

universities, and medical facilities.

The MUTCD recommends that no more than one supplemental guide sign be installed per 

interchange and not more than two destinations be listed on a supplemental guide sign. 

AASHTO also provides specific maximum distance from interchange criteria that should be met. 

The AASHTO guidelines are incorporated into the MUTCD by reference (see section 2E.35).

(2)

If a supplemental guide sign is used, it is to be placed approximately midway between advance 

guide signs for an interchange where two advance guide signs are used or 800 ft (244 m) after a 

lone advance guide sign. 

The recommended limit of one supplemental guide sign per interchange and the spacing of 

supplemental guide signs at least 800 ft (244 m) from other signs is intended to avoid possible 

driver information overload. However, there appears to be no direct empirical evidence that this 

amount of signing will mitigate information overload or that more signing than is currently 

permitted would result in information overload. Therefore a priority for the data collection 
19

portion of this project was to measure driver performance as a function of the frequency and 

spacing of guide signs, specific-service signs, and permanent overhead CMSs. In conducting this 

research, it was assumed that the driver is unfamiliar with the roadway and adjacent facilities and 

is seeking specific destinations.

APPROACH

The objectives were addressed through a series of studies. Each of these studies is the subject of 

a separate chapter. 

Chapter 2 briefly describes a laboratory study to obtain psychophysical measurements with a 

state-of-the-art NTCIP- and NEMA-compliant CMS. This effort included asking observers to 

judge the similarity of symbols and messages displayed on an actual CMS with variations of the 

same symbols and messages on a liquid crystal display (LCD). The purpose of collecting the 

similarity comparison judgments was to assess the requirements for conducting CMS messaging 

research with simulated CMS displays. 

Chapter 3 examines the legibility distance of CMS messages in relation to letter height and the 

observer’s visual acuity.

Chapter 4 describes a field test with the state-of-the-art CMS in which drivers read CMS 

messages in a controlled driving environment. Reading time, reading distance, and eye glance 

behavior were recorded as drivers approached the CMS while maneuvering an instrumented 

vehicle at a constant speed. The results are reported for phased messages, flashing messages, 

messages with abbreviations, messages of varying length, and text versus symbol messages.

Chapter 5 describes a driving simulator study that examined the following issues:

• Would drivers habituate to or lose respect for CMS messages such that a critical CMS 

message is missed, if, on a long trip (48 min), they encountered a CMS containing 

irrelevant information every 0.5 mi (0.81 km)?

• Would salient and frequently changing (every 3 s) images on a CMS compel drivers to 

look at the CMS (i.e., cause attention capture)?

• Would following distance influence glance behavior to a CMS?

Chapter 6 describes a study that is very similar to that of chapter 4. However, this chapter

examines whether the content on the CMS changes the probability that the driver will detect a 

hazard in the roadway.

Chapter 7 examines how the frequency and spacing of freeway guide signs affects driver 

behavior and the ability to detect guide sign information. 

21

CHAPTER 2. LABORATORY EVALUATION OF CMS

INTRODUCTION

Laboratory comparisons were made between messages displayed on a CMS and the same 

messages displayed on a 60-inch (1.5-m) LED/LCD monitor. The purpose of the comparisons 

was to determine which display properties of CMS messages were most important when 

emulating messages on other types of displays. There were two immediate applications for the 

findings: (1) to guide the display of CMS messages in the FHWA sign laboratory, which uses the 

LED/LCD display used in these comparisons, and (2) to guide the display of CMS messages in 

the FHWA highway driving simulator.

The FHWA Human Factors Team often evaluates new sign formats in its sign laboratory. In the 

past, these formats have been for static highway signs. In those evaluations, red, green, and blue 

color values and visual angle subtended were the primary concerns in emulating the highway 

signs. This seemed reasonable given that in the real world, the actual color values of the signs 

would vary with the available lighting, and visual angle would vary with viewer distance. 

However, modern CMSs use LEDs to emit light so available light will primarily affect contrast 

and not color. The LEDs on the Daktronics® CMS used in this study consist of one red, one 

blue, and one green LED in each pixel, and the pixels are spaced 0.79 inches (20 mm) apart, 

center to center. At sufficiently long distances, the light emitted from individual LEDs is

perceived as coming from a single source, and the individual pixels are perceived to blend 

together, much as individual color television pixels, when viewed from an appropriate distance, 

are not perceived. The present study examined whether it would be necessary to emulate the 

individual LED elements or pixels of the CMS when presenting alternative CMS message 

formats in the sign laboratory or the driving simulator. 

METHOD

Materials

The CMS was a Daktronics® VF-2320 full-color (red, green, blue (RGB)) matrix display with 

0.79-inch (20-mm) pixel pitch. The display surface was 4 by 4 ft (1.2 by 1.2 m), which 

accommodated a 64- by 64-pixel display. The display is shown in figure 19 in chapter 3.

Some messages for the CMS were developed using the Daktronics® Vanguard V4 Professional 

software. Other messages for the CMS were images of static signs adapted from the MUTCD 

and supplied by Daktronics®. Bitmaps were exported from the Daktronics® software and then 

imported into graphics software to develop messages to be displayed on the LCD/LED monitor. 

The graphics software was used to modify the imported bitmaps so that alternative methods of 

emulating the CMS display could be compared. In some cases, an image was intended to come 

as close as possible to a literal replication of the CMS display—where each pixel consisted of 

three circular RGB elements and pixels were 0.79 inches (20 mm) apart, center-to-center. In 

other cases, the emulated images were made up of circular or rectangular pixels of a uniform 

color. Finally, some images were made of filled in tracings of the imported images—that is,

individual pixels were not emulated in those images.
22

The 60-inch (152-cm) LED/LCD display was shorter and wider (29 by 52 inches (73.6 by 

132 cm)) than the CMS. To display images on the CMS that were the same size as those on the 

LCD, the CMS images were cropped to the LCD height.

Procedure

Display comparisons were made in the FHWA photometric and visibility laboratory, which has 

flat black interior walls and no windows and is sealed so that outside light is not admitted. 

During the comparisons, the only light inside the laboratory came from the two display devices, 

the CMS controller, and a laptop computer used to control the LCD. 

The intensity of the CMS was set to the lowest available setting (1 percent). At that setting, a 

white circular area with a 12-inch (30.5-cm) diameter emitted about 52.5 fl (180 cd/m2

). A 

circular area consisting of only red LEDs emitted about 14.6 fl (50 cd/m2), a blue circular area

emitted about 4.4 fl (15 cd/m2), and a green circular area emitted about 36.5 fl (125 cd/m2).

Table 1 shows the photometric measurements of the X and Y Commission Internationale de 

l'Eclairage (CIE) color coordinates of stimuli displayed on the CMS.(47) The table also shows the 

specified RGB settings that were used to generate the measured colors. 

Table 1. Measured CIE color coordinates for colors displayed on the CMS and the RGB 

settings used to generate those colors.

Color

Coordinates RGB Settings

X Y Red Green Blue

Red 0.70 0.30 255 0 0

Green 0.17 0.73 0 255 0

Blue 0.13 0.07 0 0 255

White 0.29 0.29 255 255 255

Yellow 0.43 0.52 255 255 0

Orange 0.59 0.49 255 128 0

On the LCD, a white circular area with a 12-inch (30.5-cm) diameter measured 32.4 fl (111 

cd/m2); a red circular area emitted 7.4 fl (25.2 cd/m2), green emitted 23.5 fl (80.4 cd/m2), and 

blue emitted 2.3 fl (8.0 cd/m2

). Table 2 shows the photometric measurements of the X and Y CIE 

color coordinates of colors displayed on the LED/LCD monitor.

Table 2. Measured CIE color coordinates for colors displayed on the LED/LCD and the 

RGB settings used to generate those colors.

Color

Coordinates RGB Settings

X Y Red Green Blue

Red 0.65 0.33 255 0 0

Green 0.29 0.63 0 255 0

Blue 0.15 0.06 0 0 255

White 0.28 0.28 255 255 255

Yellow 0.44 0.52 255 255 0

Orange 0.6 0.37 233 120 26
23

For each pair of stimuli presented, one stimulus on the LED/LCD and one on the CMS, 

participants were instructed to rate similarity, where a rating of 1 indicated that the pair was not 

at all similar and a rating of 7 indicated that the pair was very similar.

Participants first compared the images on the two displays from a distance of 90 ft (27 m) with 

the LCD immediately to the left (relative to the observer) of the CMS display. The images on 

both displays were at the same height above the floor, and subtended the same visual angle. 

The comparisons were then repeated with the LCD positioned 15 ft (4.6 m) from the participant 

and slightly to the participant’s left so that the LCD and the CMS, which was still 90 ft (27 m) 

away, could be viewed at the same time. The images presented at the near distance were sized so

that they subtended the same visual angle as the images on the CMS. Except for the reduced size 

of the near images, they were as similar as possible to the CMS images.

Participants

Nine individuals—FHWA Federal employees and contract staff—served as subjects.

Stimuli

To ease the burden on the reader in relating stimuli to results, the individual stimulus pairs are

described in the Results section along with the comparison findings.

RESULTS

The results for the comparisons with the two displays side by side are presented first. These are

followed by the same signs with the LCD at 15 ft (4.6 m).

Exit Closed (Side-by-Side)

There were four versions of the sign shown in figure 3. They varied only in RGB values, which 

are shown in table 3. There were no significant differences in the similarity ratings between 

versions. 

Figure 3. Photo. Message advising that exit is closed.
24

Table 3. RGB values used in comparisons with message advising that exit is closed.

Version

RGB Settings

Red Green Blue

A 240 180 10

B 255 172 0

C 255 180 64

D 255 200 20

Exit Closed (LCD Close, CMS Far)

As with the side-by-side comparison, there were four versions of the close comparison stimuli. 

Unlike the side-by-side stimuli, there was no attempt to emulate individual pixels; all characters 

were solid. An example of the stimuli as they appeared on the LCD screen is shown in figure 4. 

Figure 4. Photo. Exit closed sign example (proportionally scaled and zoomed in).

There were no significant differences in ratings among the four comparisons, which used the 

same color settings as shown in table 3. Across the four comparison stimuli, the overall mean 

rating was 4.9.

End (Side-by-Side)

The two versions of this sign are shown in figure 5 and figure 6. Version A consists of white and 

grey circles intended to simulate the appearance of individual pixels. Version B consists of red, 

green, and blue circles intended to simulate the three LEDs that make up individual pixels. Both 

images were based on the same bitmap supplied with the sign vendor’s software. 

Figure 5. Photo. Version A of the end sign.
25

Figure 6. Photo. Version B of the end sign.

Version A with a mean similarity rating of 5.0 (with a standard error of the mean (se) = 0.236) 

was rated significantly more similar to the CMS display of the bitmap than was version B, which 

was rated 4.2 (se = 0.324), F (1, 8) = 12.25, p = 0.008. This suggests that at a simulated distance 

of 90 ft (27 m), it is not necessary to simulate individual LEDs. Use of red, green, and blue 

circles considerably reduced the perceived brightness of the LCD image, which made the image 

appear grey in comparison with the nominally white CMS image.

End (LCD Close, CMS Far)

There were three stimuli for this comparison. Two were smaller versions of those shown in 

figure 5 and figure 6. The additional comparison stimulus eliminated the space between pixels 

and is shown in figure 7.

Figure 7. Photo. Solid version end sign comparison stimulus.

There were no significant differences in the ratings of the three comparison stimuli. The overall 

stimuli mean rating was 4.8.

High Wind (Side-by-Side)

There were four versions of the high wind warning sign, one of which is shown in figure 8. 

Three of the four signs displayed on the monitor were rated reasonably similar to the same sign 

on the CMS. The difference in mean rating among the four versions was significant, F(3, 24,) = 

8.67, p = 0.012. Post hoc tests showed that version C which had a more orange appearance, was 

rated lower than the other versions, F(1, 8) = 64.0, p < 0.0001. The mean and 95-percent 

confidence limits for the similarity ratings are shown in table 4.
26

Figure 8. Photo. Emulation of high wind warning sign.

Table 4. RGB values and similarity ratings of four versions of the high-wind warning sign 

with the CMS standard.

Version

RGB Settings

Mean

Lower Confidence 

Limit

Upper Confidence 

Red Green Blue Limit

A 240 240 13 5.3 4.6 6.1

B 230 246 0 5.4 4.7 6.1

C 255 200 0 4.0 3.5 4.5

D 255 255 0 5.2 4.5 5.9

High Wind (LCD Close, CMS Far)

In reducing the size of the high wind stimuli in which individual pixels were simulated, the 

brightness was reduced to the point where the LCD stimuli were barely visible. Therefore, the 

space between pixels was dropped. Version B of the comparison stimuli, as described in table 5,

is shown in figure 9. The differences between ratings were significant, by Wilk’s Lambda,

F (3, 6) = 5.7, p = 0.034. Versions B and D were rated significantly higher than A and C.

Table 5. RGB values and rating results for the close high wind comparison stimuli.

Version

RGB Settings

Mean

Lower Confidence 

Limit

Upper Confidence 

Red Green Blue Limit

A 255 246 0 4.2 3.3 5.1

B 230 246 0 5.1 4.2 6.0

C 250 240 20 4.3 3.6 5.1

D 255 255 0 4.8 3.9 5.6

Figure 9. Photo. Version B of the high wind comparison stimuli.
27

Accident Ahead Merge Left (White, Side-by-Side)

There were three versions of the accident ahead text message that were nominally white. These 

are shown in figure 10 through figure 12. One version consisted of white pixels (red, green, and 

blue for each set to 255), the second grey pixels (red, green, blue for each set to 230), and the 

third in which the space between pixels was filled in with white (red, green, and blue for each set 

to 255). There was a significant difference in similarity ratings among the three signs, 

F (2, 16) = 7.3, p = 0.006. Post hoc comparisons showed that the sign with grey pixels was rated 

significantly less similar to the CMS image than the two white versions. At a distance, the space 

between pixels, whether black or white, resulted in images that were judged equally similar. 

Comparisons were not requested with the close LCD because the small pixelated images 

appeared too dim to warrant testing.

Figure 10. Photo. Accident ahead merge left sign with white pixels that yielded a mean 

similarity rating of 5.6 between the CMS and LCD displays.

Figure 11. Photo. Accident ahead merge left sign with grey pixels that yielded a mean 

similarity rating of 4.7 between the CMS and LCD displays.

Figure 12. Photo. Accident ahead merge left sign with solid white letters that yielded a 

mean similarity rating of 5.6 between the CMS and the LCD displays.

Accident Ahead Merge Left (Orange)

Four versions of the accident ahead merge left message in various shades of orange or amber

were used, one example of which is shown in figure 13. An overall test indicated significant 

differences in mean similarity ratings, F(3, 24) = 15.1, p < 0.001. Post hoc tests showed that 

versions C and D in table 6 were rated significantly more similar to the CMS image than 

version A. Version B was not significantly different from version A. Interestingly, version B had 

the same RGB values as the standard displayed on the CMS. It appears that in this case, the 

Samsung® display required a greater amount of green to approach the perceived color on

the CMS.
28

Figure 13. Photo. Accident ahead merge left example.

Table 6. RGB values for the four orange alternatives to the accident ahead merge left 

standard. 

Version

RGB Settings

Mean

Lower Confidence 

Limit

Upper Confidence 

Red Green Blue Limit

A 240 180 10 5 4.231 5.769

B1 255 180 64 5.111 4.398 5.824

C 255 200 0 5.778 5.137 6.418

D 240 220 56 5.889 5.288 6.49 1

RGB values also for the CMS standard.

No Pedestrians

There were two LCD versions of the no pedestrians (no peds) text message, which are shown in

figure 14 and figure 15. One of the versions was made up of individual white dots aligned in the 

same manner as the pixels on the CMS. The other version was similar, but the spaces between 

pixels were filled in so that only the outer edges of characters retained the outline of the CMS 

pixels. Both versions received equal and relatively low similarity ratings (Mean (M) = 4.8).

Figure 14. Photo. No peds message on LCD with emulated CMS pixels.

Figure 15. Photo. No peds message on LCD with filled-in space between emulated CMS 

pixels.

Exit 47 B, Route 7 N, Closed

Similar to the no pedestrians sign, the Exit 47 B sign comparison was a pixelated emulation, 

shown in figure 16, and an emulation with solid letters, shown in figure 17, of the same message 

on the CMS display. The solid version received a slightly higher rating (5.3) than the pixelated 

version (5.0), although this difference did not reach statistical significance, F(1, 8) = 4.0, 

p = 0.08.
29

Figure 16. Photo. Exit 47 B message on LCD with emulated CMS pixels.

Figure 17. Photo. Exit 47 B message on LCD with filled-in space between emulated CMS 

pixels.

Shoulder Work

Four versions of a work zone warning (see figure 18) with negative contrast were compared. 

The RGB values of the LCD images and the CMS image are shown in table 7. There were no 

significant differences among the four ratings. It appears that the reasonable variation in orange 

shading of the simulated signs is not critical.

Figure 18. Photo. Shoulder work CMS message emulation on LCD.

Table 7. Ratings for negative contrast orange shoulder work sign.

Version RGB Settings Mean 

Rating

Lower Confidence 

Limit

Upper Confidence 

Red Green Blue Limit

A 255 120 26 4. 9 4.3 5.5

B1 240 133 0 5.3 4.8 5.8

C 255 138 26 4. 9 4.0 5.8

D 233 120 26 5. 3 4.6 6. 1

Setting on CMS.

DISCUSSION

The ratings suggest that few participants would mistake an LCD image for an image on the 

CMS. Only one participant gave any ratings of 7. On the other hand, many of the LCD images 

received mean ratings of 5 or more, which may suggest that the LCD may be a useful tool in 

addressing human factors issues with CMS messaging. 

Overall, the findings suggest that when simulating a CMS display, attempts to emulate individual 

pixels are counterproductive. This makes sense given that even at its lowest brightness setting, 
30

the CMS is brighter than the LCD used in these tests. Emulating CMS pixels reduces the 

brightness of the LCD even more than when all LCD pixels are used.

Subjective color matches for LCD stimuli are preferable to attempts to match the RGB settings 

of the CMS. The preference for subjective color matches can be seen in several of the results. 

For instance, the LCD accident ahead message that was judged most similar to the CMS message 

had more green in the RGB setting than did the CMS. This also makes sense given the brightness 

of the LCD color boxes compared with those of the CMS.

When emulating CMS displays, whether in the laboratory or on projected images in a driving 

simulator, the intended appearance of the CMS should be the primary concern not the absolute 

color settings on the CMS or the pixel content of the CMS display.
31

CHAPTER 3. LEGIBILITY TESTING

In addition to the psychometric tests conducted in the laboratory, the CMS was taken outside,

and the legibility distance of various messages was tested. The primary interest was the 

relationship between letter height and legibility distance. 

METHODS

Equipment

The CMS was a Daktronics® VF-2320 full-color (RGB) matrix display with 0.79-inch (20-mm) 

pixel pitch. The display surface was 4 by 4 ft (1.2 by 1.2 m), which accommodated a 64- by 

64-pixel display. The display is shown in figure 19.

Figure 19. Photo. Sign used for legibility testing.

Testing was done midday. The stimuli were four-letter words presented individually at a rate of 

1.25 words/s with a blank interval of 0.3 s between words. 

Procedure

Testing was done during daylight hours between 1 and 3 p.m. The typical ambient illuminance 

was 2,487.9 fc (26,780 lx). The sign face was oriented to the northwest, with the Sun to the 

southwest at approximately 60 degrees ±15 degrees elevation. The sign brightness was set to 

100 percent. The sign luminance measurement results for white and yellow stimuli are show in 

table 8. The luminance measures were obtained by illuminating 12-inch (30 cm) squares of the 
32

specified color at the specified locations on the display. A Konica Minolta® Chroma Meter 

CS-100 was used to take the luminance readings. The readings were taken from a distance of 

50 ft (15.2 m) with a 1-degree aperture. The luminance of a black (all pixels off) block was 67 fc 

(229 cd/m2

) which yields a mean contrast ratio of 43 for yellow characters and 46 for white 

characters.

Table 8. Luminance and CIE color coordinate measures for colors of fonts used in legibility 

testing.

Color

Units/CIE 

Coordinates

Upper 

Left

Upper 

Right Center

Lower 

Left

Lower 

Right Mean Min Max

Yellow/ 

Amber

Fl 2,948 2,884 3,153 3,298 3,211 3,099 2,884 3,298

cd/m2 10,100 9,880 10,800 11,300 11,000 10,616 9,880 11,300

X 0.419 0.431 0.423 0.427 0.436 0.4272 0.419 0.436

Y 0.528 0.518 0.524 0.524 0.517 0.5222 0.517 0.528

White

Fl 3,153 3,065 3,328 3,503 3,444 3,298 3,065 3,503

cd/m2 10,800 10,500 11,400 12,000 11,800 11,300 10,500 12,000

X 0.287 0.298 0.289 0.288 0.298 0.292 0.287 0.298

Y 0.302 0.303 0.298 0.294 0.301 0.2996 0.294 0.303

Min = Minimum.

Max = Maximum.

The three font sizes used were 7 (height) by 5 (width) pixels, 12 by 8 pixels, and 16 by 8 pixels. 

All fonts used a 2-pixel stroke width. The actual height of the characters, which were all 

displayed in uppercase, can be computed by multiplying the pixel size, 0.79 inches (20 mm), by 

the font height. However, it is common to assume that there will be some apparent blooming of 

the characters that adds to the apparent font height. Therefore, for the remainder of this section, a 

bit less than 0.5 inches (12.7 mm) of blooming is assumed so that the three fonts are referred to 

by their assumed heights: 6, 10, and 13 inches (15, 25, and 33 cm). Each font was tested once 

with amber letters (red and green LEDs that constitute a character pixel at 100 percent) and once 

with white letters (all LEDs that constitute a character pixel at 100 percent). 

Markers were laid out on the ground on a line perpendicular to the face of the sign at intervals 

that were multiples of 15 ft (4.6 m). Specific distances where markers were placed were 

180, 270, 300, 315, 360, 390, 405, 450, 525, 581, and 600 ft (55, 82, 91, 96, 110, 119, 123, 137, 

160, 177, and 183 m). On alternating trials, participants were tested at successively longer 

distances until they could identify no words, or successively shorter distances until they could 

identify all words. Each set of trials began with the participant standing at either the closest 

marker or at the 581-ft (177-m) marker. At the 581-ft (177-m) marker, a wall presented a 

physical obstruction that prevented testing at a precise multiple of 15 ft (4.6 m). Across 

participants, the beginning distances were roughly counterbalanced such that particular font sizes 

and colors were equally distributed between near or far markers.

Fifty-nine six-word lists were available. No words were repeated within a list of six words. Each 

word could appear on up to three lists but not in the same serial order across lists. 
33

Participants

Six individuals, five males and one female, were tested. All were Government or contract 

personnel who worked at the FHWA Turner-Fairbank Highway Research Center. The mean 

visual acuity of the participants was 20/17 (range 20/13 to 20/25) as assessed with the Landolt C 

module of the Freiburg Visual Acuity test.(48)

RESULTS

The mean proportions of six words that were correctly read at each distance are shown in

figure 20. The MUTCD criterion for legibility distance for letters on static signs is estimated 

using 30 ft (9 m) per inch (2.5 cm) of letter height.(2) With 6-inch (15-cm) letter height, nearly all 

words could be read at 180 ft (55 m). With 10-inch (25-cm) letters, which the MUTCD method 

suggests should be legible at 300 ft (91 m), 92 percent of white lists and 100 percent of amber 

lists were read correctly. The MUTCD method suggests 13-inch (33-cm) letters should yield a 

legibility distance of 390 ft (119 m). At that distance, 100 percent of white lists and amber lists 

were read correctly. At 581 ft (177 m), 89 percent of 13-inch (33-cm) white character lists were 

read correctly.

1 inch = 2.54 cm

1 ft = 0.305 m

Figure 20. Graph. Legibility testing results.

Because the visual acuity measure used here is linear, and the relationship between letter height 

and legibility distance is linear, the CMS results can be easily compared with the MUTCD 

guidance for estimating legibility distance of standard highway signs of 30 ft/inch of letter height

and assumes a minimum visual acuity of 20/40.
34

Assuming 90-percent correct word identification as the criterion for legibility distance, table 9

shows the results for this study for participants with a mean visual acuity of 20/17 and the linear 

extrapolation to what the results would have been with individuals having an acuity of 20/40, 

which is the minimum acuity required for a driver’s license in most States.

Table 9. Observed legibility and linear transform of legibility distances for persons with 

20/40 visual acuity.

Character Height

(inches)

Observed With Mean 20/17 

Acuity Estimate for 20/40 Vision

Distance (ft)

Legibility 

Distance (ft/inch)

Distance 

(ft)

Legibility Distance 

(ft/inch)

6 220 37 94 16

10 460 46 196 20

13 590 45 251 19

1 inch = 2.54 cm

1 ft = .305 m; 

1 ft/inch = 0.12 m/cm

Because of factors such as small sample size (n = 6) and testing a limited number of fixed 

differences, the results are not perfectly linear. However, they suggest that for persons with 

normal vision, the CMS characters yield a legibility distance of 40 to 45 ft/inch (4.8 to 5.4 m/cm) 

of letter height, and for persons with 20/40 vision 20 ft/inch (2.4 m/cm) would yield a reasonable 

estimate of legibility distance.

DISCUSSION

These results suggest the 0.79-inch (20-mm) pixel pitch full-color display used in this test 

provides a longer legibility distance than the previous generation of CMS displays that used a 

1.6-inch (40-mm) pixel pitch with amber LEDs.(1) They also suggest that for the display type 

used, drivers with approximately 20/20 vision, the 90- to 100-percentile legibility distance can be 

conservatively estimated using a factor of 40 ft/inch of letter height (9 m/2.5 cm). It appears that 

amber characters may have slightly longer legibility distance than white characters, even though 

the amber characters use one-third fewer LEDs than the white characters.

The test reported in this chapter used participants who were standing still and not looking 

through a windshield. This would be expected to yield somewhat more accurate and somewhat 

longer legibility distances than those obtained from observers in a moving vehicle. The next 

chapter reports on legibility distances for stimuli viewed by drivers of a moving vehicle.
35

CHAPTER 4. CMS FIELD TEST

INTRODUCTION

The relevant properties of CMSs were reviewed in chapter 1. Chapter 4 describes testing of the 

effects of those properties on sign comprehension or message-recognition distance while drivers 

are engaged in navigating a shallow slalom course.

METHODS

The data were collected on a closed course while participants drove an instrumented vehicle that 

was equipped with a dashboard-mounted eye-tracking system.

Participants

Useable data were obtained from nine participants (six males and three females). The mean age 

of participants was 39 years old (minimum = 19, maximum = 61). Participants were paid $40/h

for 3 to 5 h of participation. All drivers were licensed in Virginia.

Before they were scheduled for testing, participants provided signed permission for the 

investigators to obtain their records from the Virginia Department of Motor Vehicles. Individuals 

with more than one moving violation or police-reported crash in the preceding year or a driving 

while intoxicated violation in the preceding 3 years were excluded from participation. Upon 

arrival at the test facility, participants read and signed an inform consent form. 

Test Facility

Testing was conducted on a 30-ft- (9-m-) wide drag strip. A CMS was placed on the left side of 

the drag strip 1,250 ft (376 m) from the start line. Traffic cones were placed on the track to form 

a 12-ft- (3.6-m-) wide lane that curved first from the left to the right side of the track, then back 

to the left, and ended in the middle of the track at the CMS location. The arrangement of the 

traffic cones is shown in figure 21. The purpose of the curved path was to require participants to 

attend to lane keeping in addition to viewing messages displayed on the sign.

Changeable Message Sign

The CMS was the same one used in the laboratory study (see chapter 2) and is shown in

figure 22.
36

Figure 21. Photo. Layout of course on drag strip.

Figure 22. Photo. The CMS used in the study.

The brightness of the sign was set at 100 percent because the tests were conducted in daylight 

between 9 a.m. and 1 p.m. in fall 2012. At the 100-percent brightness setting, a white stimulus 
37

12 inches (30 cm) in diameter measured between 3,065 and 3,502 fl (10,500 and 12,000 cd/m2

)

depending on which location on the display was measured. A red stimulus measured a mean of 

1,051 fl (3,600 cd/m2

) and an amber stimulus a mean of 3,094 fl (10,600 cd/m2). Laboratory 

testing, reported elsewhere, showed that the display was compliant with NEMA standards for 

LED color displays intended for highway applications.(23)

Research Vehicle

Participants drove a 5-year-old full-sized sports utility vehicle equipped with a dashboardmounted, three-camera eye-tracking system.(49) The system sampled eye vectors and head 

position at 60 Hz. Three scene cameras mounted on the roof of the vehicle, directly over the 

driver’s head, recorded about 80 degrees (horizontal) of the driver’s view of the road ahead. In 

post processing, the scene camera view was merged with the eye-tracking vectors. In addition to 

the eye-tracking data, direction measuring equipment, Global Positioning System, and 

accelerometer data were recorded and synchronized.

The sound recording capability of the eye-tracking system was not functional at the time these 

tests were conducted. Therefore, to capture participants’ verbal responses, the experimenter 

operated a handheld voice recorder during each trial. Recordings were started as the participant 

maneuvered the research vehicle to the start line and stopped after the participant correctly spoke 

the message on the sign.

Message Types

Message Length

Current guidance suggests that CMS messages should be limited to three units of information. 

The unit of information concept is probably appropriate for most drivers, at least in the case 

where the posted information matches drivers’ expectations. If the posted information does not 

match the typical questions a motorist might have (e.g., What? Where? Action?), then the 

number of words in the message would be a better metric for message length. In this experiment, 

the number of words on the CMS was varied from one through seven. Reading time and glance 

behavior were assessed for all participants with all message lengths. Because of the small size of 

the CMS that was used, the smallest available font size was used (7 pixels high by 6 pixels 

wide). The stroke width was 2 pixels and letter height was about 5.5 inches (140 mm). With 

allowance for some blooming of pixels, the letter height would be equivalent to 6-inch (152-mm) 

letters on a static sign. Only the first letter of each word was capitalized. Preliminary testing with 

stationary observers (see chapter 2) showed that these words became legible at 180 ft (55 m) for 

observers with 20/20 vision. All words displayed were appropriate for traffic-related messages, 

but the order of words was randomized so that each word represented one information unit. Each 

word was presented centered on a line. The following is the complete list of words used:

• Accident.

• Ahead.

• Blocked.

• Caution.

• Lane.
38

• Left.

• Merge.

• Move.

• Slow.

• Traffic.

• Use.

For any particular message, words were selected from the list and ordered randomly. Each 

participant received a different set of randomized words. To illustrate the font used for all 

messages, figure 23 shows one of the three-word messages that was used. 

Figure 23. Photo. Example of a three-word message.

Flashing

The effects of flashing text were evaluated by displaying the same messages three different 

ways: (1) static messages (no flashing), (2) first of three lines flashing, or (3) entire message 

flashing. The flash rate was 1 Hz (on 0.05 s, off 0.5s). The following messages were used with 

slashes indicating line breaks:

• CRASH/MERGE/LEFT.

• DETOUR/EXIT 67/CLOSED.

• RIGHT/LANE/BLOCKED.

• TIME TO/RESTON/36 MIN.

• TRAFFIC/JAM/BE CALM.

• STOPPED/TRAFFIC/AHEAD.

• CRASH/USE ALT/ROUTE.

The fonts were all 14- by 10-pixel approximations of FHWA type E font. All messages were 

white (i.e., each illuminated pixel consisted of a fully illuminated red, green, and blue LEDs). 

Individual participants saw individual messages only once, with the messages randomly assigned 

to flash mode with the restriction that there was an equal number of trials in each mode.
39

Symbols Versus Text

This test compared gaze behavior and reading time between pairs of symbol images of traffic 

signs from the MUTCD and the equivalent text messages for the same signs. Except for the 

interstate shield text equivalent, all text messages used a 14- by 10-pixel approximation of the 

FHWA type E font. The text messages were all displayed with positive contrast. The text color 

was appropriate for the type of message: yellow for warnings, white for the interstate 

designation, and orange for the work zone sign. The symbol signs and text equivalents are listed 

in table 10.

Table 10. Signs used for comparisons of symbol- and text-sign reading time and glance 

behavior.

MUTCD Symbol Sign 

Designation Text Alternative

W2-1 intersection ahead

M1-2 interstate shield (I-95)

W2-4R right lane ends

W3-5 speed reduction (45 mi/h)

W2-6 roundabout ahead

W3-1 stop ahead

W3-3 signal ahead

W21-1a workers

1 mi/h = 1.6 km/h

Abbreviations

To test the effect of abbreviations on gaze behavior and reading time, MUTCD-approved 

abbreviations for use on portable message signs were used. The abbreviations used and the 

phrases in which they were used are shown in table 11.

Table 11. Vocabulary for abbreviations test.

Word Abbreviation Message Context

Road RD ROAD CLOSED

Avenue AVE PARK AVENUE

Center CNTR CENTER LANE CLOSED

Normal NORM NORMAL TRAFFIC

Feet FT 100 FEET

South S I-495 SOUTH

Mile MI NO EXIT NEXT 60 MILES

1 ft = 0.305 m

1 mi = 1.6 km

With one exception, three fonts were used in the abbreviation test: (1) an approximation of 

FHWA series E that was 14 pixels high by 10 pixels wide, with a stroke width of 2 pixels; (2) an 

approximation of FHWA series B that was also 14 pixels by 10 pixels with a 2-pixel stroke 

width; and (3) a manufacturer-supplied 14- by 8-pixel font with a 2-pixel stroke width. Thus, all 

three fonts were 11 inches high (or 12 inches if a blooming effect is assumed), and all had a 

2-pixel stroke width. The choice of font for particular messages was based on the need to use 
40

only whole words on a line and the limitation to three lines of text with adequate spacing 

between lines. The one exception was the abbreviation for south. To approximate the FHWA 

interstate shield as closely as possible, “I-495 S” and “I-495 SOUTH” were displayed using 

two fonts: I-495 was in a 16- by 10-pixel font with a 3-pixel stroke width, and SOUTH and S 

were displayed in a 16- by 8-pixel font with a 2-pixel stroke width.

Phasing or Paging

When a message is too long to fit on one screen, an option is to display the message in a 

sequence of screens. This is referred to as phasing. The MUTCD limits phasing to two screens or 

pages.(2) Each screen must be interpretable by itself, and the order in which the two screens are 

read should not matter. The manual indicates that the legibility distance of messages should be 

greater (larger text should be used) when messages are phased because it will take drivers more 

time to read the message. However, the manual provides no guidance on how much larger the 

text should be and indicates that letter heights greater than 18 inches (46 cm) will not improve 

legibility distance.

The two-phase messages used in this study were compliant with the manual’s regulations and 

guidance. Only two- and three-line messages were displayed in any single phase. Two phases 

were used, with each phase displayed for 3 and 0.3 s of blank screen between phases. Each phase 

was intended to be understandable without reference to the other phase. Only one unit of 

information was displayed on a line. The messages used in the phasing trials are shown in

table 12. Rather than compare the same eight messages in two phases against the same messages 

presented in one phase, the static messages from the flashing set of trials were used as 

comparison items. Although not a perfect match, static messages contained a similar number of 

words and information units (see the bulleted list in the previous section Flashing).

Table 12. Eight two-phase messages used for phasing trials.

Phase Message Used

Phase 1 USE

EXIT 27

CRASH

SR 123

KEEP

RIGHT

SLOW

TO

25 MPH

Phase 2 CIRCUS

TRAFFIC

USE

SR 267

DEBRIS

IN ROAD

JAM

AHEAD

Phase 1 POLICE

ACTION

PREPARE

TO STOP

ROAD

CLOSED

SIGNAL

OUT

Phase 2 DO NOT

ENTER

ROAD

BLOCK

USE

EXIT 2

4-WAY

STOP
41

Table 13. Comparison of static message length to phased message length.

Static Message

Number 

of Words

Information 

Units Phased Message

Number 

of Words

Information 

Units

Crash Merge Left 3 2 Circus Traffic 

Use Exit 27

5 2

Detour Exit 67 Closed 4 3 Crash SR 123 

Use SR 267

6 3

Right Lane Blocked 3 1 Keep Right 

Debris in Road

5 2

Road Work Ahead 3 2 Slow to 25 mph 

Jam Ahead

6 2

Time to Reston 36 

Minutes

5 2 Police Action 

Do Not Enter

5 2

Traffic Jam Be Calm 4 2 Prepare to Stop 

Road Block

5 2

Stopped Traffic 

Ahead

3 2 Road Closed 

Use Exit 2

5 2

Crash Use Alt Route 4 2 Signal Out 

4-Way Stop

4 2

Mean 3.625 2 Mean 5.125 2.125

1 mi/h = 1.6 km/h

Font

When viewed from an appropriately long distance, full-matrix CMSs are capable of emulating 

FHWA typefaces that include lowercase text. In search tasks, such as looking for a street name 

on navigation signs, uppercase/lowercase text can result in longer recognition distances, 

presumably because the shape of words with ascending and descending characters can be 

recognized before the individual letters become identifiable. Unfortunately, the number of fonts 

with true ascenders and descenders was limited with the equipment provided for this test. The 

messages used in the font comparison trials are listed in table 14. Only the uppercase/lowercase 

“left lane block” and “normal traffic” messages contained true ascenders. None of the messages 

contained descenders (e.g., the letter “p” in open did not descend below the line of the other 

characters). Thus, the current test examined the effects of uppercase versus pseudo mixed-case 

messages. 

Table 14. Messages used for comparison of recognition performance between all uppercase 

messages and messages in pseudo uppercase/lowercase.

Uppercase Lowercase

HOT LANE OPEN HOT Lane Open

LEFT LANE BLOCK Left Lane Block

ROAD WORK NEXT Road Work Next

SLOW SPEED 1 MI Slow Speed 1 Mi

NO EXIT NEXT 60 MILES No Exit Next 60 Miles

NORMAL TRAFFIC Normal Traffic
42

Participants were exposed to both uppercase and mixed case versions of each message. The order 

of mixed case and all uppercase versions was varied across participants, and the different 

versions of the same message were separated by at least five trials with unrelated messages, 

which included messages related to other issues (e.g., phasing or message length).

Test Procedures 

A total of 146 messages were prepared. Individual participants were each shown about 60 of the 

messages (range 55 to 70) such that each sign was shown approximately the same number of 

times across all participants. The order in which signs were shown was randomized for each 

participant, subject to the constraint that the same message in a different format (e.g., font, case, 

and abbreviation) did not occur more than once in any series of five messages.

Participants were instructed to begin each trial at the approach to the left start line. There they 

waited for an experimenter in the backseat to signal that the data recording equipment was ready. 

At that point, participants were to briskly accelerate to 25 mi/h (40 km/h) and to maintain that 

speed while they attempted to read the CMS message. Participants were instructed to read the 

message aloud as soon as practicable while still maintaining 25 mi/h (40 km/h) and staying 

within the marked lane.

RESULTS

The data recorded by the eye-tracking system were analyzed using software that related regions 

of interest (ROIs) marked by an analyst on video records of the forward view to gaze vectors 

determined by the eye-tracking software. For this study, one ROI was marked for each trial. This 

ROI covered a rectangular area about the sign that subtended approximately 2 degrees of visual 

angle regardless of the distance from the sign. A representative ROI captured at three locations 

within a trial is shown in figure 24 through figure 26. Figure 24 is at the beginning of the trial, 

approximately 1,250 ft (381 m) from the sign. Figure 26 shows the same ROI just before the sign 

is passed. The 2-degree size of the ROIs was chosen for two reasons: (1) it represents what is 

generally considered the area subtended by the fovea, the region of the retina used to capture 

scene details; and (2) it represents the approximate radial accuracy of the dashboard-mounted 

eye-tracking system.
43

Figure 24. Screen capture. Two-degree ROI for the CMS at a distance of approximately 

1,250 ft (381 m).

Figure 25. Screen capture. Two-degree ROI for the CMS approximately halfway down the 

slalom course.

Figure 26. Screen capture. Two-degree ROI for the CMS toward the end of a run.

The primary measure of eye-movement behavior was the look. A look was defined as any 

accumulation of four or more hits on an ROI within a series of six frames (one frame equals 

0.017 s; six frames equals 102 ms). A look began when this criterion was first met and 

terminated when the number of hits within the preceding six frames dropped below four. The 

number of looks and the duration of looks were analyzed.

In addition to eye-tracking measurements, two measures were extracted from the voice 

recordings of participants: (1) trial duration—time from the beginning of a trial until the 

participant completed correctly reading aloud the message on the sign and (2) response 

duration—the time between when the participant began repeating the message aloud and when 

the participant completed reading the message. All measures were to the nearest second. 
44

Response durations of zero length could result when responses were rounded to the nearest 

second. Trials always began with the vehicle at a full stop. The beginning of trials were marked 

when the sound of the engine revving was noted in the recordings.

All statistical tests employed General Estimating Equation (GEE) models. Analyses of frequency 

data assumed a Poisson distribution. Analyses of look durations assumed a gamma distribution. 

All analyses of vocal response assumed a Poisson distribution. 

Note that in evaluating time differences, the vehicle traveled at about 25 mi/h (40 km/h) so each 

second that elapsed equated to about 37 ft (11 m) traveled.

All error bars shown in the charts below represent 95-percent confidence limits about the 

expected means.

Message Length

Neither the mean number of looks to the sign nor the average duration of each look varied 

significantly as a function of message length. As can be seen in table 15, the mean number of 

looks was surprisingly large, and the mean look duration was brief.

Table 15. Number of looks to messages and look duration as a function of the number of 

words in the messages.

Looks

Message Length (Number of Words)

1 2 3 4 5 6 7

Number of looks 30 38 35 28 37 39 51

Mean duration of looks (s) 0.120 0.104 0.123 0.116 0.118 0.135 0.135

Given the large number of looks—far more than would be necessary to read a seven-word 

message—it appears that many of the looks were made for the purpose of determining whether 

the sign was near enough to be read. Standard road signs typically get far fewer looks, probably 

because drivers have far more experience reading standard signs and thus know when they 

should become legible, but also because drivers are not typically tasked to read standard signs as 

soon as possible, which was the instruction for reading the CMS.(50) The relatively short mean 

durations are not concealing single long glances. Across all participants and all message lengths, 

the longest recorded look was 0.46 s to a six-word message.

There was a significant effect of message length on trial duration, 2

(6) = 92.5, p < 0.001. This 

effect can be seen in figure 27. Response duration showed a similar significant trend,

2

(6) = 123.9, p < 0.001. Response durations are shown in figure 28. These two measures share 

the same end-of-response time and are correlated to the extent that the start of reading time is 

independent of message length. It appears the measures are highly correlated.

χ

χ
45

Figure 27. Graph. Expected mean trial duration as a function of the number of words in 

the CMS message.

Figure 28. Graph. Expected mean response duration as a function of the number of words 

in the CMS message.

Flashing

The duration of looks and number of looks did not vary significantly at the p < 0.05 level. 

However, given the small sample size in the study, it may be worth reporting a trend favoring 

static and all-lines-flashing messages over messages with the first line flashing. The duration of

looks to static and all-lines-flashing messages were nearly the same, 0.13 s. The expected mean 

duration of looks in which the first line flashed and the second and third lines were static was 
46

0.17 s. The test for differences between the means approached significance, 2

(2) = 5.48, p = 

0.06. There was also a non-significant trend ( 2(2) = 3.78, p =0.15) for first-line-flashing 

messages to receive more looks. The expected mean number of looks is shown in table 16 as a 

function of message type.

Table 16. Expected mean number looks to messages in the flashing text trials.

Message Type

Expected Mean

Number of Looks

Lower 

.95 Confidence 

Interval Limit

Upper 

.95 Confidence 

Interval Limit

Static 31 24 40

First Line Flashing 36 29 44

All Lines Flashing 27 20 35

The voice response main effects for both trial duration ( 2(2) = 13.2, p = 0.001) and response 

duration ( 2

(2) = 8.5, p = 0.015) were significant, and support the contention that static or all 

flashing messages result in more efficient message transmission than first-line-flashing 

messages. Figure 29 shows the expected mean trial duration, and the expected mean response 

duration. From the start of the trial, responses were completed about 1.5 s later when all lines 

were flashing than when all lines were static or only the first line flashed. Responses were 

longest when only the first line flashed.

Figure 29. Chart. Trial duration and response duration as a function of flashing mode.

Symbols Versus Text

There were no significant differences in look durations or number of looks between symbol signs 

and their text equivalents.

χ

χ

χ

χ
47

Participants responded about 3 s sooner (i.e., trial duration was 3 s shorter) to the symbol signs 

(expected mean trial duration, 12.1 s ±3.8) than they did to the text-based equivalents of those 

signs (expected mean, 15.4 s ±3.25), 2(1) = 6.4, p = 0.012. 

Although the symbol signs’ meanings were generally recognized sooner, and thus yielded shorter 

trial durations than the text equivalents, the workers symbol sign was an exception. As can be 

seen in figure 30, the mean trial duration for the workers symbol sign was more than twice that 

for the text based version, 2(1) = 16.4, p < 0.001. 

The total number of looks at the workers symbol sign was significantly greater than to its text 

equivalent, 2(1) = 13.5, p < 0.001. 

Although the workers symbol sign was probably legible from as great or greater distance than 

the ROAD WORK sign, several participants struggled to interpret the sign and made comments 

such as “what is that?”, and then made several incorrect guesses before generating a plausibly 

correct response such as “person sweeping.” This suggests that the symbol is not easily 

comprehended.

Figure 30. Chart. Comparison of trial durations for workers symbol signs with other text 

and symbol signs.

Abbreviations

There were no significant differences in look durations or number of looks between messages 

with FHWA-approved abbreviations and complete word messages. Also, there were no 

significant differences in either trial duration or response duration.

Phasing or Paging

As shown in figure 31, there were significantly more looks at two-phase messages than onephase messages, 2

(1) = 8.77, p = 0.003. The duration of these looks did not vary significantly 

(static message expected mean = 0.136 s, phased message expected mean = 0.151 s). 

χ

χ

χ

χ
48

Figure 31. Chart. Expected mean number of looks at CMSs as a function of the number of 

message phases.

Trial durations were longer by about 3.5 s with phased messages than with static messages,

2

(1) = 12.0, p = 0.001.

Case

There were no significant effects observed in the font trials—look duration, number of looks, 

trial duration, and response duration effects were all non-significant. 

DISCUSSION AND CONCLUSIONS

In these tests, drivers made a large number of looks to the CMS. Because they were instructed to 

read aloud the messages as soon as possible, the simplest explanation for this is that participants 

needed to repeatedly look at the sign to determine when they could begin responding. Whether 

this finding has any practical significance for CMS applications is uncertain. For static signs, far 

fewer looks are typically observed. However, drivers presumably have a great deal of experience 

with static signs, so they may have reasonably good expectations regarding when they should 

attend to them. Also, the messages on static signs are predictable from context and are fairly 

standard, so a brief look may be enough to confirm a driver’s expectations. CMSs vary more 

than static signs in legibility distance, partly because the technology used varies and, at least for 

older CMSs, the legibility distance decreases with the age of the sign. For these reasons, drivers 

may start looking at CMSs as soon as they are detected. To the extent that CMS messages are 

less predictable than static sign messages, drivers may need to maximize the distance at which 

they attend to messages to allow for the extra processing (i.e., reading time) unpredictable 

messages will require.

On-road eye-tracking observation in which drivers were not instructed to read CMS messages 

would be needed to clarify whether CMS messages get more looks than static signs. However, in 

χ
49

the driving simulation experiments described in the following chapters, large numbers of looks at

simulated CMSs were not observed. In fact, there was some evidence that drivers in those studies 

could read unexpected CMS messages without fixating directly on the signs. In those studies, the 

messages were in large letters that could be read from at least 800 ft (244 m) away and were 

positioned over the roadway. 

Message Length

The duration of trials increased with the number of words in messages. However, the differences 

in response duration and trial duration were negligible when messages contained two to 

five words. Six- and seven-word messages required 4 or 5 s more response time than shorter 

messages. Although the FHWA guidance on limiting the number of units of information on a 

CMS to three is valid, the findings of this study suggest that the total number of words is also 

important and should be limited to five or fewer whenever possible.

Flashing

First-line-flashing messages required more looks than static or all-lines-flashing messages. Firstline-flashing messages also had longer response durations than the other two modes. The alllines-flashing mode had the longest delay before participants completed responses (trial 

duration). Together these findings support the MUTCD prohibition of flashing messages.(2)

Symbols Versus Text

The present findings support the use of symbols on CMSs, at least in the case where the symbols 

are already familiar to drivers. The advantage of symbols is that they are recognizable from 

longer distances than are text messages that require similar display area. The finding that the 

ROAD WORK symbol sign yielded worse performance than its text-based alternative is 

important. To provide an advantage, symbols must be familiar or easily comprehended. Symbols 

that are not quickly comprehended by a large percentage of the population may result in driver 

distraction while drivers contemplate the symbol’s intended meaning. For standard warning 

signs, placards are sometimes used to familiarize drivers with the meaning of novel symbols. 

This practice would not be appropriate for symbols on CMSs, because the use of text with 

symbols would necessitate reducing the size and thus the legibility distance of both text and 

symbols.

Abbreviations

The present findings support current FHWA policy regarding the use of abbreviations as 

specified in the MUTCD.

(2) Approved abbreviations appear to have no effect on driver 

performance compared with full-text messages. Abbreviations not approved for use on portable 

messages signs were not evaluated in the present study, and therefore these findings should not 

be generalized to abbreviations not in the MUTCD.
50

Phasing or Paging

Phased messages take longer to read than static messages and should be avoided. In the present 

tests, all two-phase messages were successfully read. The CMS was within an unobstructed 

line-of-sight for 1,250 ft (381 m), and the vehicle was traveling at 37 ft/s (11 m/s) so that the 

drivers had about 30 s to view the messages, although the messages may not have been legible 

for the entire distance. In real-world driving, other vehicles, roadway geometry, and other TCDs

may limit viewing distance more than observed in these tests. If two-phase messages are used, it 

should not be assumed that drivers will have time to safely read them. Therefore, safety-critical 

messages should not be phased.

Case

All performance differences between uppercase and proper case (uppercase/lowercase) fonts 

were non-significant. With all four metrics, the actual differences favored the uppercase 

messages. These results provide no reason to change the current policy, as specified in the 

MUTCD, which favors the use of only uppercase messages except when emulating guide 

signs.(2)

The CMS used in this study, a full-color, full-matrix, LED display with 0.79-inch (20-mm)

pixels, is legible from distances approaching those of static highway signs. The capability to 

display symbols and provide high-contrast messages in text in most lighting conditions, is 

expected to greatly increase the effectiveness of CMS messaging compared with earlier CMS 

technologies.
51

CHAPTER 5. THE EFFECT OF REPEATED IRRELEVANT CMS MESSAGING ON 

THE DETECTION OF SAFETY-CRITICAL MESSAGING

INTRODUCTION

This experiment was one of a series to determine how signing within the right-of-way affects 

driver behavior. The focus of this experiment was to examine the potential for driving-irrelevant 

information on a CMS to cause drivers to lose respect for traffic-related messages on CMSs.

The present study had two purposes: (1) to document how driver gaze behavior would be 

affected by different information types on CMSs and (2) to evaluate whether drivers might be 

more inclined to ignore critical safety-related messages if frequently exposed to drivingirrelevant information on CMSs.

METHODS

The FHWA highway driving simulator was used to simulate CMS messaging in a freeway 

environment. An eight-lane freeway was simulated (four lanes in each direction). An overhead 

CMS was located every 0.5 mi (0.8 km) over a distance of 48.5 mi (78.1 km). One group of 

participants was presented with CMSs that displayed frequently changing faces every mile and 

travel-time messages interspersed between the faces signs. Another group saw blank overhead 

signs every mile with travel-time messages interspersed between the blank signs. The 97th sign 

was the same for all drivers. The three-line message on that sign read “ACCIDENT 

AHEAD/ALL LANES CLOSED/USE NEXT EXIT.” Two major hypotheses were tested: 

(1) whether drivers looked more at signs with frequently changing salient color images than at

blank or travel-time signs, which might suggest the salient images distract drivers from their 

primary task—monitoring the road ahead and (2) whether drivers exposed to visually salient 

non-traffic-related messages on overhead signs would habituate to or lose respect for the 

overhead signs and thus fail to detect a critical instruction to exit the freeway. The relationship 

between following distance and gaze behavior was also examined. 

The Simulator

The simulator’s screen consisted of a 240-degree portion of a cylinder with a radius of 8.9 ft 

(2.7 m). Directly in front of the driver, the design eye point of the simulator was 9.5 ft (3 m) 

from the screen. The stimuli were projected onto the screen by five BarcoTM projectors with 

resolutions of 2,048 pixels horizontally by 1,536 pixels vertically. Participants sat in a late-model 

compact sedan as shown in figure 32. The simulator’s motion base was not enabled in this 

experiment. The car’s instrument panel, steering, brake, and accelerator pedal all functioned in a 

manner similar to real-world compact cars.
52

Figure 32. Photo. FHWA highway driving simulator.

The simulated vehicle was equipped with a hidden intercom system to enable communications 

between the participant and a researcher who ran the experiment from a control room. The 

researcher in the control room could also view the face video from the eye-tracking system and 

thereby monitor the participant’s wellbeing.

The Simulation Scenario

A 1.5-mi (1.6-km) section of freeway without overhead signs preceded the first CMS. Each CMS

spanned all four lanes and approximated the dimensions of a sign 56 ft (17 m) wide by 8.6 ft 

(2.5 m) high. Because of limitations in the resolution of the simulator’s projectors, all signs in 

the simulator were oversized so that their legibility distance approximated real-world legibility 

distances. In this experiment, signs were 1.75 times the size of their real-world equivalent. Thus, 

the simulated overhead signs were sized to approximate the legibility distance of an overhead 

sign of approximately 32 ft (10 m) wide by 5 ft (2 m) high. A sign of this size with 0.79-inch

(20-mm) pixel pitch would enable a display of 488 pixels horizontally and 76 pixels vertically.

Before being made oversize, the simulated letter height of the CMS text was 18 inches (46 cm).

Two scenarios defined the between-groups experimental manipulation. One scenario included 

human faces on every other CMS. The other scenarios simulated no message (blank) on every 

other CMS. In both scenarios, travel times to a hypothetical destination were displayed between 

the signs that defined the experimental manipulation. A typical travel-time message is shown in 

figure 33.
53

Figure 33. Photo. Travel time to McLean was displayed once per mile.

Thirty-four faces were displayed in sequence on each of the faces signs. Each face was displayed 

for 3 s, and the entire series of faces repeated throughout the experiment. It has been shown that 

human faces attract and hold attention as few other stimuli can.(51,52) The face stimuli were 

captured from two sources: non-copyrighted celebrity photographs from the Internet and selected 

faces from the International Affective Picture System.(53) On the signs that displayed faces, the 

backgrounds also varied. Thus, on the approach to any faces sign, the participant might be 

exposed to four or more faces. The location of the faces on the signs varied, either left, center, or 

right such that faces photographed from the left side were displayed on the right side of the 

display, faces photographed straight on were centered on the display, and faces photographed on 

the right side were displayed on the left side of the display. A representative faces sign is 

depicted in figure 34.

Figure 34. Photo. Faces serving as salient but driving-irrelevant information.

The 97th sign, with the instruction to exit the freeway, is depicted in figure 35. To make the 

driving task more realistic and visually demanding, vehicle traffic was simulated. The VISSIM 

traffic model was used to generate the behavior of vehicles in the traffic stream. Because the 

random number seed for the traffic model was always the same, all participants were immersed 

in the same traffic stream. However, because participants controlled their own speed, 
54

acceleration, and lane choice, participants could experience different traffic conditions in their 

immediate surroundings. 

Figure 35. Photo. Accident ahead message.

At the beginning of test sessions, 5,000 vehicles/h (1,250 vehicles/h per lane) were generated 

for 6.7 min. Participants were instructed to begin driving 2.5 min into this period of traffic 

generation. Thereafter, approximately every 3 mi (4 km), 500 vehicles/h would exit the freeway 

at off ramps, and 500 vehicles/h would merge into the traffic stream from on ramps. One elderly 

participant was reluctant to drive at the posted speed limit of 65 mi/h (105 km/h) and eventually 

was passed by the entire traffic stream. That participant was replaced. There were off and on 

ramps every 1.5 mi (2 km), although the traffic model populated only half of them with traffic.

Participants were instructed to maintain 65 mi/h and to drive in the second lane from the right, 

except when they wanted to pass. This instruction resulted in most participants staying within the 

initially generated traffic flow throughout the experiment. The full instructions were as follows:

You will be driving on a freeway that has four lanes in the direction you are 

traveling. There will be other vehicles on the road, although traffic should not be 

heavy. You should obey the law and drive as you normally would. The posted 

speed limit is 65 miles per hour. Please drive in the second lane from the right 

except when you want to pass slower moving vehicles. Your destination is 

McLean. This is a relatively long trip and may take an hour. We will be recording 

how you drive and where you look. Please drive as you normally would. 

However, if you arrive at your destination (McLean) within 3 minutes of the time 

you would arrive if driving at the posted speed limit, you will receive a $10

bonus. Do you have any questions?

For most of the 51 mi (82 km) of simulated freeway, the speed of other vehicles followed the 

cumulative probability distribution shown in table 17. However, congestion was simulated 

beginning 2,081 ft (634 m) upstream of sign 6 and again every 15 signs (upstream of signs 

21, 36, 51, 66, and 81). Upon entering congested areas, each simulated vehicle decelerated at a 

desired rate of 6.6 ft/s (2.0 m/s). After decelerating in these areas, all other vehicles traveled 
55

between 30 and 45 mi/h (72 and 48 km/h) according to the desired cumulative probability 

distribution shown in table 18. Each congestion area continued for 1,200 ft (366 m) and was 

followed by a 300-ft (91-m) zone in which vehicles accelerated back toward their normal desired 

speed. Note that the desired speed or acceleration might not have been realized where another 

leading vehicle provided impedance. In addition, simulated traffic changed lanes to achieve its

desired speed or to prepare to exit. The congested areas were included to challenge participants’ 

attention and reduce boredom on the drive, which lasted approximately 45 min.

Table 17. Desired speed cumulative probability distribution of the simulated traffic stream.

Speed

(mi/h)

Cumulative 

Probability

50.0–55.4 0.01

55.5–59.9 0.04

60.0–64.0 0.08

64.1–66.5 0.12

66.6–70.0 0.50

70.1–75.0 0.75

75.1–80.0 1.00

1 mi/h = 1.61 km/h

Table 18. Desired speed cumulative probability distribution at congestion locations.

Speed

(mi/h)

Cumulative 

Probability

30.0–33.6 0.05

33.7–36.1 0.17

36.2–37.6 0.46

37.7–38.5 0.65

38.6–40.0 0.85

40.1–42.5 0.95

42.6–45.0 1.00

1 mi/h = 1.61 km/h

A short practice session preceded the test session. The original purpose of the practice session 

was to enable the participants to become accustomed to the handling characteristics of the 

simulated vehicle. However, pilot testing had shown that some participants thought they were 

supposed to stay on the freeway, regardless of CMS warnings. Therefore, the training session 

was modified to ensure that participants knew that it was expected that they should follow 

instructions on the CMSs. The modified training included a minimum of two CMSs that 

instructed participants to take the next exit. The practice session instructions were as follows:

You will be driving on a simulated freeway that has no other traffic. The purpose 

of this five minute drive is to allow you to familiarize yourself with the steering, 

acceleration and braking of the simulated vehicle. At the start, you will be stopped 

in the second lane from the right. When we are ready, I will ask you to accelerate 

slowly to 25 mile per hour and drive within a lane. You will then be asked to 

brake to a full stop. After that, you will be asked to accelerate to 65 miles per hour 
56

and continue driving in the second lane from the right. There will be a changeable 

message sign with a traffic alert about a bridge out on your route. Follow the 

detour instructions on that sign.

In fact, once on the detour, another CMS was encountered that directed participants back onto 

the original route. In the practice session, if a participant failed to follow a detour instruction, the 

researcher would urge the participant to follow the directions on the next CMS that he or she

encountered. In this way, all participants saw and followed the instructions of at least two detour 

messages on CMSs before beginning the main test session. Those who initially failed to follow 

the CMS directions were presented with three or more dynamic messages with detour 

instructions. The purpose of this practice was to maximize the probability that participants who 

read the final message sign in the test session (about 48 min later) would feel obligated to follow 

its instruction.

Eye-Tracking System

The simulator was equipped with a four-camera dashboard-mounted eye-tracking system that 

sampled at 120 Hz.(49) The system tracked horizontal gaze direction from approximately the 

location of the right outside mirror to the left outside mirror and vertical gaze direction from the 

instrument panel to the top of the windshield. Gaze direction accuracy varied by participant. The 

mean accuracy of gaze position across participants was 1.6 degrees (radius) with a 0.7-degree

standard deviation. The eye-tracking data (e.g., gaze direction of each eye, head position, etc.) 

were merged with data from the simulator (e.g., vehicle speed, lane position, and steering wheel 

position) and the current forward view of the simulation visual scene (approximately 60 degrees 

horizontal by 40 degrees vertical). In addition, a separate dataset was recorded by the driving 

simulator of the distance between the front bumper of the participant’s vehicle and the nearest 

simulated vehicle in the participant vehicle’s forward path. 

To quantify when and for how long participants looked at each CMS, a researcher used analysis 

software to indicate an ROI on individual frames of the recorded video image. An example of an 

ROI is shown in figure 36 (the halo around the sign). ROIs were created for the first 11 CMSs

(signs 0–10), and signs 17–25, 32–40, 47–55, 62–70, 77–85, and 94–96. For each of these 

CMSs, glances at the ROIs were recorded for the last 10 s before the CMS began to pass out of 

the driver’s view. This resulted in sampling an equal number of travel-time and experimentally 

manipulated signs at intervals throughout the session so that trends over time in glance behavior 

could be observed. Note that none of the zones coded with ROIs coincided with the zones that 

simulated congested traffic.
57

Figure 36. Photo. ROI indicated on CMS.

Participants

A total of 32 participants—16 males and 16 females—completed the study. All were licensed 

drivers from the Washington, DC, metropolitan area. The mean age of participants was 47 years

(range 20–85). Twenty-four participants provided interpretable eye-tracking data. Otherwise 

useable data were obtained from eight participants for whom eye tracking was unsuccessful. The 

mean age of the 24 participants with good eye-tracking data was 45 years (range 20–79 years)

and 11 were male. Only one participant reported a mild simulator sickness symptom (headache),

and no participant dropped out as a result of simulator sickness.

RESULTS

Throughout this chapter, error bars in the charts and graphs represent 95-percent confidence 

limits around the means.

Response to the Incident-Related Detour Message

Of the 24 drivers for whom eye-tracking data were also available, 7 failed to respond to the 

message on the 97th sign by exiting the freeway. These results are shown in table 19. The 

difference in exit-taking behavior between blank and faces groups was not statistically 

significant by Fisher’s Exact Test. Because there is an apparent trend, even if non-significant, for 

more drivers to fail to exit in the faces group, a second test was done that included all drivers 

who completed the study, regardless of the quality of their eye-tracking data. The data for this 

test are shown in table 20. With the additional participants included, there was no difference 

between the group presented with faces and that presented with blank signs (apparent or 

otherwise). 
58

Table 19. Response to warning to take next exit by drivers for whom eye-tracking data

were available.

Sign Type Failed to Exit Exited Total

Blank signs 2 10 12

Faces signs 5 7 12

Total 7 17 24

Table 20. Response to warning to take next exit by all drivers who completed the drive.

Sign Type Failed to Exit Exited Total

Blank signs 4 12 16

Faces signs 5 11 16

Total 9 23 32

The eye-tracking evidence for looking at the CMS is summarized in the following subsections. 

However, most of the participants were also asked during the post-experiment debriefing if they 

had read the message on the 97th sign. All participants who took the exit said that they had read 

the message—which seems reasonable given that no participant had taken any of the preceding 

48 exits and the 49th exit differed only in that it was preceded by the accident ahead message. Of

the nine drivers who failed to exit, seven were asked if they had read the message, and all but 

one of those also claimed to have read it. The one driver who claimed not to have noticed the 

warning was in the blank sign group. 

Gaze Behavior

Gaze location was measured for the 10 s prior to reaching the point where the sign passed out of 

the driver’s field of view. The areas defined by the 10 s approach are hereafter referred to as 

“data collection zones.”

There are numerous issues to be considered in the analysis of eye-tracking data. The mean 

accuracy of the gaze location in this study was 1.6 degrees. Because foveal vision is limited to 

about 2 degrees of visual angle, examination of fine detail requires shifting the gaze to within 

2 degrees of the details. At an actual or simulated distance of 1,000 ft (305 m), 2 degrees of 

visual angle includes an area of about 35 ft (11 m) in diameter. On a flat, level road, a driver 

whose gaze is centered on a vehicle 1,000 ft (305 m) ahead would also include three travel lanes 

and any sign within 17 ft (5 m) of the center of gaze within foveal vision. The lower edge of the 

simulated signs was 17 ft (5 m) above the travel lanes. Traveling at 65 mi/h (105 km/h), as 

participants were instructed to do in this experiment, 10 s of travel time would traverse 950 ft 

(290 m) of roadway. 

Because the eye tracker sampled at 120 Hz, there was a new estimate of the center of gaze every 

0.008 s. Despite the limits in accuracy, precision, and the size of the foveal area subtended at 

long distances, it can be expected that over time, the average gaze position will fall on the object 

of visual regard. Therefore the problem is to determine which 120-Hz hits on a target should 

be counted and which disregarded as noise or error. The analyses that follow employed 

three different methods for assessing when participants were looking at a CMS. These methods 

are referred to as glance, look, and fixation. They were defined as follows:
59

• Glance. Any accumulation of 12 (12 × 0.008 s = 100 ms) or more hits on an ROI, where 

a hit is a single 120-Hz center of gaze estimate that falls on the sign’s ROI. This is the 

least restrictive definition of a gaze at a sign; it maximizes both the probability of 

correctly detecting a gaze as well as the probability of incorrectly identifying a gaze at

something else as a gaze at the sign. For any ROI, this measure was binary; either a 

glance was recorded or it was not.

• Look. Any accumulation of 7 or more hits on an ROI within a series of 12 frames 

(100 ms). A look began when this criterion was first met and terminated when the 

number of hits within the preceding 12 frames dropped below 7. This criterion is more 

conservative than that for a glance but not as restrictive as the definition of a fixation. 

Also, unlike a fixation, a single look may include more than one gaze at different areas of 

a sign.

• Fixation. Seven consecutive gaze vector hits (60 ms) within a fixation radius of 4 percent 

of the vertical image height (i.e., 15 pixels on the 372-pixel image) and centered on the 

first of the 7 gaze positions that designated the start of a fixation. The fixation continued 

until there were six consecutive hits (50 ms) outside the fixation radius. For a simulated 

object 500 ft (152 m) ahead, the fixation radius subtended a visual angle of about 

2 degrees. A fixation on an ROI was recorded if the center of the fixation was on the

ROI. In practice, the fixation criterion is quite conservative in identifying a gaze at an 

ROI (relatively low probability of correct identification and relatively high probability of 

a miss) in part because of the restriction that the gaze should remain within a small area 

and therefore could not capture pursuit movements.

The convergence of the three ROI gaze criteria on the same conclusion should increase 

confidence in the research findings.

As indicated in the Eye Tracking section, gaze data were analyzed for a subset of 59 signs. 

Six data collection zones included the final portions of the simulated congestion. Given the 

proximity of these six zones to simulated congestion, it might be expected that time headways 

would be shorter there than in the remaining free-flow data collection zones. Because the 

experimental design includes repeated measurements and the time headways were not normally 

distributed, a GEE model was used to test for differences in headway as a function of data 

collection zone type. In this model, a gamma response distribution was assumed, and an identity 

link function was used. A gamma distribution was used because headways can never be zero or 

negative. For a discussion of GEE, distribution choices, and link functions, see Stokes, Davis, 

and Koch.(54) Mean headway in the 52 free-flow data collection zones, not including the zone 

with the accident ahead sign, was 335 ft (102 m). In the six zones in proximity to congestion, the 

mean headway was 241 ft (73 m). This difference was statistically significant, 2

(1) = 15.98, 

p < 0.001. Subsequently, analyses of glance, looks, and fixations were conducted separately for 

the 6 congestion zones and the remaining 52 free-flow zones. Where differences in gaze patterns 

were not significantly different between free-flow and congested zones, the data were combined.

χ
60

Relationship Between Exit Taking and Gaze Behavior

All three measures of gaze behavior were used to assess whether exiting after passing the 

accident ahead message was related to whether participants directed their gaze to the message. 

No measure (i.e., glances, glance duration, number of looks, duration of looks, number of 

fixations, or duration of fixations) was related to whether or not participants took the exit.

Table 21 shows the distribution of participants who took the next exit as a function of whether a 

glance was recorded. Although one participant who did not exit said she did not see the accident 

ahead sign, the glance data suggested otherwise. The participant who did not take the exit and 

did not have a recorded glance claimed to have seen the message and, in fact, attempted to exit 

but decided that traffic in the right lane prevented him from exiting. 

Table 22 summarizes the various gaze metrics related to the critical CMS message. Note that the 

expected mean durations in table 22 include durations of 0.00001 s for participants who did not 

glance at the critical sign.

Table 21. Relationship between glance to the accident ahead message and taking exit.

Driver Action

Glance 

Recorded

No Glance 

Recorded

Took exit 16 3

Did not take exit 4 1

Table 22. Mean counts and mean durations for various gaze metrics to the accident ahead 

message.

Gaze Metric Mean

Standard 

Deviation

Glance duration (s) 0.98 0.98

Number of looks 4.00 4.74

Look duration (s) 0.23 0.30

Number of fixations 2.58 2.47

Fixation duration (s) 0.27 0.29

Glance Results

The entire experimental drive lasted about 45 min. With sessions of this length, there was some 

concern that glance behavior might vary over time because of fatigue or boredom or because the 

perceived value of the sign content changed over time. To test these hypotheses, the proportion 

of signs glanced at in each of the seven groupings of zones described in the eye-tracking system

section was examined. GEE models were used because the glance probability data contained 

repeated measurements and did not appear normally distributed. The order analyses assumed a 

binomial probability distribution with a logit (log odds) link function. The explanatory variables 

in the model were order (1, 2, 3, 4, 5, 6, or 7) and order squared (to test for linear and curvilinear 

order effects). The sign displaying the detour warning was excluded from the analyses. Results 

indicated that there were no linear or curvilinear trends in probability of glancing at signs as a 

function of order.
61

When closely following another vehicle, drivers might have less spare visual capacity for 

attending to CMSs. Because different individuals adopt different car-following strategies and 

because traffic conditions could vary along the route, time headway was considered in the 

analyses of the gaze data. Time headways were considered short if less than 1.5 s and long if 

greater than that duration. Over all data collection zones and all drivers, 75 percent of headways 

were greater than or equal to 1.5 s. 

Each data collection zone was classified as containing a glance to the CMS in that zone 

(glance = 1) or not. GEE models were used to test the effects of sign content type, time headway, 

and their interaction on the probability of a glance. These analyses assumed a binomial response 

distribution with a logit link function. 

Preliminary analysis showed there was no difference in the probability of a glance between faces 

and travel-time signs in either the 6 congested zones or the 52 free-flow zones. Nor were there 

glance probability differences between congested and free-flow zones. Therefore, zones with 

faces and travel-time signs were combined into a non-blank class, and the congestion 

classification was not used. The GEE analysis included sign type (blank or non-blank), headway 

(short or long), and the interaction of sign type and headway. The interaction was not significant. 

Participants had a higher probability of glancing toward the non-blank signs (Pe = 0.55) than 

toward the blank signs (expected probability (Pe = 0.21), 2(1) = 9.11, p = 0.003). Participants 

had a higher probability of glancing toward a CMS when driving with a long headway 

(Pe = 0.49) than when driving with a short headway (Pe = 0.38), 2 (1) = 4.07, p = 0.044.

Given that a participant glanced at a CMS, the distances at which the glance started and ended 

were examined. Glance start distance was not significantly affected by sign content, traffic, or 

time headway. The mean glance start distance was 701 ft (214 m) before the CMS

(25th percentile = 555 ft (169 m), 75th percentile = 901 ft (275 m)).

Expected mean glance end distance was related to sign content, 2(2) = 31.97, p < 0.001. As can 

be seen in figure 37, expected mean glance endings were farthest from blank signs and occurred 

closest with faces signs. Post hoc tests showed all three end-distance means were significantly 

different from each other. 

χ

χ

χ
62

1 ft = 0.305 m

Figure 37. Chart. Expected mean distance from CMS at the last glance.

Beginning and ending glance distances were based on individual 120-Hz gaze samples at signs 

that received a glance. The location of the beginning and end of a glance did not provide an 

indication of the amount of time participants were glancing at the signs. To assess the amount of 

time participants gazed at the signs, the glance duration was computed using only those signs 

within a content type that received a glance. Recall that glance duration is the sum of all 

120-Hz samples for which a gaze at the sign was recorded, regardless of when in the data 

collection zone these samples were taken. As a result, glance duration could be the sum of 

one continuous gaze or multiple 1/120-s gazes several seconds apart. A GEE model with gaze 

duration as the dependent measure and sign content, headway, and the interaction of sign content 

and headway as predictors were tested. The GEE model assumed a gamma response distribution 

with an identity link function. Sign content, headway, and the interaction effects were not 

significant. The estimated mean glance duration was 0.79 s (25th percentile = 0.50 s, 

75th percentile = 1.08 s). 

Look Results

Within each data collection zone, the number of looks at the CMS was calculated as a function of 

headway. The analysis included time headway as a grouping variable, where headways greater 

than 1.5 s were classified as long, and headways less than that were classified as short. 

GEE models were used for hypothesis testing because the data contained repeated measurements 

and did not follow a Gaussian distribution.

Look Probability

The probability of at least one look at each sign was analyzed as a function of headway, sign 

content, and their interaction. Preliminary analyses showed no significant difference in the 

probability of looks at faces and travel-time signs, so in the GEE model that is reported, those 
63

two zones were combined, and the sign content variable became a comparison of blank signs 

with non-blank signs. The GEE model assumed a binomial distribution with logit link function. 

As can be seen in figure 38, both main effects were significant: participants were more likely to 

look at least once at non-blank signs than blank signs, 2 (1) = 4.35, p = 0.037, and at either type 

of sign when headways were long than when headways were short, 2 (1) = 31.31, p < 0.001.

Figure 38. Chart. Expected probability and 95-percent confidence limits of at least one look 

at a CMS as a function of sign content (blank or not blank) and mean headway.

Number of Looks

The first model examined whether the number of looks at the CMS differed as a function of sign 

content and headway. This analysis excluded the six data collections zones where congestion 

was simulated, because the short headways in those zones attenuated the headway effect. As can 

be seen in figure 39, there was a significant interaction between sign content and headway,

2

(1) = 9.62, p = 0.002. Participants were more likely to take more looks at faces signs when 

headways were long than when headways were short, and took few looks at blank signs 

(regardless of headway). Faces and travel-time sign zones were compared in the same manner as 

blank and faces zones. In that analysis, the interaction of headway with the sign content did not 

reach statistical significance, 2 (1) = 3.24, p > 0.05. However, the trend was the same as in the 

faces zones, with more looks at travel-time messages when headway was long than when it 

was short.

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Figure 39. Chart. Estimated mean number of looks and 95-percent confidence limits as a 

function of sign content and time headway.

Look Duration

The duration of looks at the CMS was computed for all looks. (When the participant did not look 

at a sign, the duration was coded as missing.) The expected mean look durations computed from 

GEE models that assumed a gamma distribution with identity link function are shown in

figure 40. An unexpected interaction between sign content and headway was found, 2(1) = 6.34, 

p = 0.012, in which blank signs received shorter glances with short headways than with long, 

whereas travel-time and faces signs received longer glances with short headways than with long. 

All expected mean look durations fell within a narrow range of 160 to 210 ms, so it is not clear 

that this statistically significant interaction has practical significance, particularly in light of the 

low probability of looks with short headways.

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Figure 40. Chart. Expected mean look duration as a function of sign content and time

headway.

Fixation Results

Fixation Probability

For each participant, the probability of at least one fixation on each CMS was calculated, and 

each data collection zone was classified as having either short or long mean time headway. 

Whether or not at least one fixation had occurred (fixation = 0 or 1) was the predicted variable in 

a GEE model that assumed a binomial response distribution with a logit link function. Predictor 

variables were headway, sign content, and the interaction of headway and sign content. 

Preliminary analyses indicated that congestion was not a significant factor so the analysis 

included all data collection zones and congestion was not included in the model. The only 

significant factor in the model was headway, 2(1) = 37.94, p < 0.001. With headways greater 

than 1.5 s, the probability of at least one fixation on the CMS approached 0.3, whereas with a 

shorter headway, the probability of a fixation was about 0.1.

Number of Fixations

A GEE model with headway, sign content, and their interaction was used to analyze the number 

of fixations on each sign, given that at least one fixation was recorded. The GEE models 

assumed a Poisson response distribution with a log link function. No significant effects were 

obtained. Overall, for signs that received at least one fixation, the mean number of fixations was 

2.27 (95-percent confidence limits 1.84 to 2.80 fixations).

Fixation Duration

Within each data collection zone, the duration of each fixation was calculated. Headway, sign 

content, and their interaction served as predictor variables in a GEE model that assumed a 

gamma response distribution with an identity link function. Only the effect of sign content was 

statistically significant, 2

(2) = 11.20, p = 0.004. As can be seen in figure 41, mean fixation 

durations were longest for blank signs and shortest for faces signs. It should be noted that 

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although the software algorithm used to identify fixations was set to detect fixations as short as 

60 ms, the shortest fixation captured was 110 ms. 

Figure 41. Chart. Expected mean and 95-percent confidence limits for fixation duration as 

a function of data collection zone and headway.

Mean fixation durations were short, and fixations away from the forward roadway of this 

duration were generally considered safe. Furthermore, for most of the approach distance, the 

overhead signs fell within what would generally be considered the forward roadway (i.e., within 

2 degrees of the horizon in the direction of travel). A few long, potentially unsafe fixations on 

CMSs were observed, but the percentage of fixations longer than 2 s was less than 1 percent. 

Recent analyses by Liang, Lee, and Yekhshatyan suggest that gaze fixations away from the 

forward roadway greater than 2 s greatly increase the odds of a crash, whereas glances away 

from the forward roadway, even those between 1.5 and 2 s, may be associated with no increase 

in crash risk.(55) Whether those recent analyses apply to CMSs above the roadway is unclear, 

because CMSs might have fallen within what those previous researchers considered the forward 

roadway or road center. The longest CMS fixation identified was 2.86 s. Table 23 shows the 

frequency distribution for fixation durations for the current study.

Table 23. Distribution of fixation durations.

Duration Frequency Percentage

Less than 1 s 1,153 92.09

Between 1 and 1.5 s 65 5.19

Between 1.5 and 2 s 22 1.76

Greater than 2 s 12 0.96

Total 1,252 100.00

Table 24 shows the number of fixations with durations greater than 2 s as a function of sign 

content and time headway. 
67

Table 24. Number of fixations longer than 2 s as a function of time headway and sign 

content type.

Headway 

Length

Sign Content

Blank Faces Time Warn Total

Short headway 2 0 0 0 2

Long headway 2 1 6 1 10

Total 4 1 6 1 12

Driving Performance Measures

The plan for this study included two measures of driving performance that might be affected by 

sign content—possibly because attending to or avoiding attending to content might increase 

driver workload. These measures were steering entropy and speed. Unfortunately, an upgrade to 

the driving simulator to increase the realism of the steering wheel feel resulted in a loss of the 

data channel used to measure steering angle and therefore entropy. Speed was examined to detect 

either decreases or variability in speed to enable greater attention to sign content.

There were no significant effects of mean speed or standard deviation of speed as a function of 

either sign group (blank versus faces) or sign content (travel time versus other), or the interaction 

of these variables.

DISCUSSION

Respect for CMS as TCDs

The majority of drivers heeded the accident ahead message that directed them to take the next 

exit. Only one of the drivers who failed to take the exit claimed to be unaware of the message. 

Indeed, several of the drivers who did not take the exit were observed trying to change into the 

exit lane. This suggests that placing non-traffic-related messages on overhead signs will not 

necessarily lead drivers to ignore these signs. Likewise, the frequently occurring travel-time 

messages did not show evidence of leading drivers to ignore more safety or operationally critical 

messages.

No evidence of habituation or loss of respect for CMS messaging was evidenced in this 45 min 

drive with CMSs recurring every 0.5 mi (0.8 km). The following two caveats to generalization of 

this finding are warranted: (1) the base case did not include a 45-min drive in which there was no 

CMS preceding the warning, and (2) these results apply to a single drive, not weeks, months, or 

years of driving under signs with irrelevant messages. 

Overall, 28 percent of participants who were exposed to 96 CMSs failed to exit. Only 

one participant claimed not to have read the message, and the glance data indicated that that 

participant had gazed at the message, although this does not imply the she read it.

Although this study cannot address long-term habituation, it did look for changes in glance 

behavior over the 45-min drive and did not detect evidence of the beginning of such a change. 

The study should be taken as positive evidence that frequently occurring CMSs that meet a 
68

traffic operations and safety need, such as CMSs for active traffic management, should not 

induce habituation or loss of respect for TCDs.

Gaze Behavior

Three measures of gaze behavior were used because it was uncertain whether any single measure 

would unambiguously characterize gaze behavior in the presence of CMSs.

The glance and look measures suggested that drivers are more likely to look at non-blank signs, a 

finding that makes intuitive sense. All three measures showed a strong effect of headway—when 

headways are short, drivers are less likely to divert their gaze from the roadway to a CMS. 

Drivers seem to regulate their gaze behavior according to the demands of the driving situation. 

Even the frequently changing faces signs did not compel drivers to divert their attention away 

from the driving task.

The look measure suggested that the faces signs were more likely to receive visual attention 

when the roadway demands for attention were low (i.e., time headway was greater than 1.5 s). 

Given that change and that faces are generally considered to have high saliency, the look finding 

was not unexpected. However, the glance and fixation measures did not reinforce the suggestion 

that changing faces attract visual attention more than static travel-time messages. 

The fixation measure is probably the most conservative of the three gaze criteria. That is, it is 

less likely to incorrectly identify the focus of visual attention and most likely to miss short gazes 

at moving objects. Nonetheless, it was surprising to find that the longest fixations were to blank 

signs and the shortest to faces signs. This effect was small and probably not worth interpreting in 

the absence of additional replications. In addition, the total time that blank signs were looked at 

across all drivers and all blank signs was miniscule compared with the total time spent with 

gazing on non-blank signs. In this study, the amount of time gazing at the sky, grass, or trees was 

not recorded. If it had been, it is probable that gazes at them would have exceeded 2 s. Drivers 

look away from the road ahead for various reasons. This does not imply that they are not 

attending to the forward roadway. Fixations on the blank signs may have been random fixations 

to relieve boredom or maintain overall awareness of the environment, and were not necessarily 

instances of distraction. 

Overall, there was little evidence to suggest that CMSs, regardless of the content, are distracting. 

Participants appear to have attended to the signs primarily when the visual demands of the 

primary driving task were low. Furthermore, both measures of gaze duration suggest that drivers 

do not dwell on signs for periods of time that would compromise safety. The next study 

examines whether the workload imposed by attention to CMSs detracts from detection of safetycritical events in the roadway.

Drivers appear to glance at CMSs when driving demands are low. This is presumably because 

drivers give priority to attending to safety-critical aspects of the driving task. When the driving 

demands are high, drivers have little spare capacity to attend to CMS messages. This implies that 

when a traffic manager posts what is considered a safety-critical message, that message should 

be tailored to minimize demands on drivers’ attention. The more that attention demands of a 

message increase, the less likely drivers are to have sufficient spare capacity to process the 
69

message. Several FHWA publications are available to provide guidance on how to minimize the 

attention demands of CMS messages.(3,5) Conversely, if drivers expect CMS messages to be 

noncritical or irrelevant, then the probability that safety-critical messages will be ignored may 

increase. The experiment described in chapter 6 was intended to test this hypothesis.

71

CHAPTER 6. THE EFFECT OF CMS INFORMATION ON DETECTION OF SAFETYCRITICAL EVENTS IN THE ROADWAY

INTRODUCTION

The previous chapter reported on a test of the hypothesis that driving-irrelevant CMS content 

would cause drivers to lose respect for the signs and then miss important traffic-related 

messages. The findings did not support that hypothesis. This chapter tests the hypothesis that 

CMS content distracts drivers from attending to safety-critical information in the roadway. A 

spilled load of logs was simulated in the roadway 300 ft (91 m) upstream of a CMS. The spilled 

load was in an area where the previous study showed that glances at the signs were most likely. 

That is, first glances at a CMS occurred between 901 and 555 ft (275 and 169 m) before reaching 

the CMS, and last glances occurred in the range 590 ft to 180 ft (180 and 55 m). The primary 

dependent measure was whether or not the driver avoided hitting the spilled load by changing 

lanes, braking, or a combination of these responses. As in the previous experiment, glance 

behavior and speed were also assessed.

To assess whether salient driving-irrelevant content might be more visually distracting than 

travel-time information or blank CMSs, the sign content that was visible when the logs came into 

the line-of-sight was varied among participants. To assess whether distraction effects might be 

greater near the beginning or end of a trip, the location of the spilled load was also varied 

between groups. There are several reasons that the signs might be more distracting at the 

beginning or end of the trip. The signs might be more distracting at the beginning of a trip if their 

novelty attracts attention. None of the drivers in the previous experiment participated in this 

experiment, so initially the faces signs might be expected to be novel. Signs might be more 

distracting at the end of the trip because drivers might seek additional stimulation as they begin 

to feel bored or fatigued. Finding a difference in spilled load response between the beginning and 

end of the drive would not test the involvement of novelty or fatigue as distraction facilitators, 

but it might indicate where to look for distraction effects in future studies.

In the experiment reported in chapter 5, the finding that faces and travel-time signs attracted

similar amounts of attention suggests that the frequently changing faces displays were weak in 

capturing attention. In the present experiment, a face-recognition test was administered after 

participants finished the experimental drive. Participants were not informed in advance that they 

would be tested for recall of the pictures shown on the overhead signs. The purpose of the test 

was to provide another measure of the degree to which the face stimuli captured attention.

METHOD

The same driving simulator and eye-tracking system were used in this experiment as were used 

the experiment reported in chapter 5. 

The blank, travel-time, and faces signs were used again in this experiment. However, in this 

experiment, all participants were presented with all three CMS content types. Each of the content 

types appeared on every third sign. As described in the following subsections, the content of the 

first sign in this sequence depended on the group to which the participant was assigned.
72

The Simulation

The simulated freeway was the same as that described previously except that the drive was 

reduced to 37 mi (59.5 km) and 72 CMSs. There were two between-group conditions: 

(1) whether the CMS beyond the spilled load of logs displayed the faces, the travel-time, or the 

blank content and (2) whether the spilled load was before the 4th CMS encountered or before the 

72d CMS. An example of the appearance of the spilled load is shown in figure 42.

Figure 42. Screen capture. Driver’s view of spilled load from 128 ft (39 m).

Traffic was generated at a rate of 5,000 vehicles per hour for the first 6.7 min. Vehicles entered 

and exited from every other ramp intersection at a rate of 500 vehicles/h. Because the spilled 

load was in the second lane from the right, participants were instructed prior beginning the test to 

drive in that lane whenever it was safe to do so and to try to maintain the posted speed of 

65 mi/h. Participants were told that they would receive a $10 bonus if they drove in the 

instructed lane and maintained 65 mi/h whenever possible. To prevent slower vehicles from 

motivating participants to change lanes, the minimum desired speed of all other vehicles was set 

to 69 mi/h. Fifty-five percent of simulated other vehicles were programmed to seek to travel 

69 to 71.7 mi/h, while the remaining vehicles were programmed to seek speeds between 

71.7 mi/h and 80 mi/h. In the left-most lane, all vehicles were set to maintain 80 mi/h. 

In the data collection zones where the spilled load was placed, other traffic began clearing the 

lane containing the logs 1,211 ft (369 m) before the logs. This distance, plus the average time

headway of 559 ft (170 m), ensured the participants had adequate sight distance to detect the logs 

and respond to them by hard braking or changing lanes.

There were zones in which all traffic slowed markedly. These zones were in the same locations 

relative to the start of the simulation as in the previous experiment. Because the scenario was 

shorter than in the experiment reported in chapter 5, there were only 5 congestion locations, the 

first located between the 4th and 5th CMS, and thereafter there was 1 congestion zone for every 

15 CMSs (e.g., the next congestion zone was between the 19th and 20th CMS). The purpose of 

these zones was to keep the participants engaged in the driving task. Gaze behavior was not 

analyzed in these zones.
73

Recognition Test

Upon exiting the driving simulator, participants were shown a series of 64 pictures on a laptop 

computer. Half of the pictures (32) were pictures of faces that had been displayed during the 

drive. The other half were foils (similar face pictures that had not been shown during the drive). 

For celebrities and other well-known persons (e.g., Barack Obama, Elizabeth Taylor, and Prince 

Charles), a different picture of the same individual was included as a foil for the picture in the 

simulation. For other images, the similarity of the foils was based on salient characteristics of the 

target pictures (e.g., hair color and style, nationality of dress, and facial expression). Each picture 

(whether target or foil) was presented individually, and participants were asked to indicate “yes” 

if they had seen the picture during the drive or “no” if they did not recall the picture from the 

overhead signs. Participants were instructed to guess when they were not sure.

Participants

Complete data, including interpretable eye tracking, were obtained from 73 participants. A total 

of 80 participants (51 males and 29 females) completed the drive and provided behavioral 

performance data. The median age of the participants who completed the test was 33.5 years 

(range 18 to 73 years). An additional five participants failed to complete the study, two because 

of simulator sickness symptoms and three because of driving simulator hardware or software 

failures.

RESULTS

Throughout this report, error bars in charts and graphs represent 95-percent confidence limits 

around the means.

Response to the Encounter With a Spilled Load

The spilled load presented the participants with a challenging task as evidenced by 26 percent of 

drivers hitting the logs. However, the message content of the CMS that was visible as the spilled 

load was approached had no significant effect on whether the logs were avoided. Nor did the 

location of the logs have a significant effect on the probability of hitting the logs. Table 25 shows 

the frequency of participants hitting or avoiding the logs as a function of CMS content and 

location of the spill.

Table 25. Number of participants who hit or avoided spilled load shown as a function of 

load location and CMS content.

Location in 

Drive Time Avoided/Hit

Message Content

Blank Travel Time Face Total

Early

Avoided 5 11 8 24

Hit 7 2 4 13

Total 12 13 12 37

Late

Avoided 12 12 11 35

Hit 3 3 2 8

Total 15 15 13 43
74

Gaze Behavior

Gaze behavior was scored for a subset of 27 of the 72 CMSs in the study. Gaze was scored for 

signs 1–9, 16–18, 32–34, 48–50, and 64–72. The same three measures of gaze behavior were 

examined: glances, looks, and fixations. All data collection zones began 10 s upstream of the 

point where the sign passed from view and ended when the sign passed from view. There were 

nine zones with each type of sign content.

Glance Results

The probability of glancing at each sign was examined as a function of sign content (faces, travel 

time, or blank) and time headway. Each sign was classified by whether it received a glance 

(no = 0, yes = 1) and by whether the mean headway in the data collections zone (i.e., 1.5 s or 

less) or long (i.e., greater than 1.5 s). The data were analyzed using a GEE model that assumed a 

binomial distribution with a logit link function. Sign content, headway, and their interaction were 

modeled as predictors of the probability of a glance. Only the effect of sign content was 

significant, 2 (2) = 41.06, p < 0.001. As can be seen in figure 43, the probability of a glance at

faces and travel-time signs was about the same, and the probability of a glance at blank signs was 

about half that for the non-blank signs. These findings are quite similar to those for the previous 

experiment when headways were long. 

There were two important differences between this and the experiment reported in chapter 5 that 

may have resulted in the failure to find a headway effect in this experiment. First, in this 

experiment, outside the congestion areas, other traffic always traveled at speeds greater than 

65 mi/h, whereas 12 percent of traffic traveled at less than 65 mi/h in the previous experiment. 

Second, the data collection areas in this experiment did not contain, and were not near, congested 

zones where headways would shrink. As a result, there were few opportunities for short 

headways in this experiment. Mean headway distance in this experiment was 559 ft (170 m) 

compared with 326 ft (99 m) in the previous experiment. Because headway had no effect on the 

glance results, headway was dropped from the subsequent look and fixation analyses.

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Figure 43. Chart. Predicted probability and confidence limits for a glance at a CMS as a 

function of sign content.

Glance duration was computed as the sum of all 0.0083-s glance vectors to the sign ROIs. The 

GEE models with glance duration as the predicted variable and sign content, headway, and the 

content by headway interaction were tested. These models assumed a gamma distribution with 

identity link function. Because the gamma distribution does not include zero, two durations were 

computed for each ROI: (1) the first method included all ROIs regardless of whether they 

received a glance, and where no glances were recorded, glance duration was assigned a duration 

of 0.00001 s; and (2) the second measure included only ROIs that received a glance. Both 

models led to similar conclusions. Only the sign content main effects were significant: (1) with 

zero durations coded as 0.00001 s, 2

(2) = 41.2, p < 0.001, and (2) with zero durations coded as 

missing, 2

(2) = 27.96, p < 0.001. Figure 44 shows predicted glance durations and their 

confidence limits by both methods of computing glance duration. By either method, the 

durations to faces and travel-time signs were not significantly different from each other, and the 

durations of glances at blank signs were significantly less than at the non-blank signs.

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Figure 44. Chart. Expected mean duration of glances at CMSs and confidence limits as a 

function of sign content.

The expected distance, in feet, for the end of a glance is shown in figure 45. The effect of sign 

content on end-of-glance distance was significant, 2

(2) = 31.18, p < 0.001. Post hoc tests 

indicated that glance-end distance to blank signs was significantly greater than glance-end 

distance to travel-time or faces signs, which were not significantly different from each other. 

1 ft = 0.305 m

Figure 45. Chart. Expected glance-end distance as a function of sign content.

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Look Results

The probability that a participant would look at a CMS was modeled with sign content as the 

predictor. A binomial response distribution and logit link function were assumed. The sign 

content main effect was significant; 2

(2) = 31.83, p < 0.001. Post hoc comparisons indicated 

that the probability of looking toward a blank sign was significantly less than the probability of 

looking toward a faces sign, 2(1) = 27.39, p < 0.001; or travel-time sign, 2(2) = 30.66, 

p < 0.001. The difference between the probability of looking at faces signs and at travel-time

signs was not significant. The predicted means and respective confidences limits are shown in 

figure 46.

The number of looks at each CMS was modeled as a function of sign content. The GEE model 

assumed a Poisson response distribution and log link function. Sign content was significant;

2

(2) = 25.55, p < 0.001. Post hoc comparisons indicated that the number of looks toward a 

blank sign was significantly less than the number of looks toward a faces sign, 2

(1) = 25.52, 

p < 0.001, or a travel-time sign, 2(1) = 21.40, p < 0.001. The difference in the number of looks 

at faces signs and at travel-time signs was not significant. The predicted means and the 

confidence limits for those means are shown in figure 47.

Figure 46. Chart. Probability of at least one look at a CMS as a function of type of sign 

content.

χ

χ χ

χ

χ

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Figure 47. Chart. Predicted number of looks at a CMS as a function of type of sign content.

The number of looks at signs that received at least one look and the average duration of those 

looks were also modeled. GEE was used to model the number of looks assuming a Poisson 

response distribution and log link function. Given that there was at least one look at a sign, 

knowing the content of the sign did not add additional predictive information. The predicted 

mean number of glances given at least one look is shown in figure 48. The duration of individual 

looks to the CMSs was modeled to determine whether sign content had a significant effect on 

look duration. Only signs that the participant looked at (i.e., for which at least one look was 

recorded) were included in the analysis. A gamma response distribution and identity link 

function were assumed. The resulting expected mean look durations are shown in figure 49. Sign 

content was a significant predictor of look duration, 2 (2) = 6.58, p = 0.037. Post hoc 

comparisons suggest that the main effect was the results for looks at blank signs, which were

significantly shorter than looks at faces signs, 2 = 6.50, p = 0.011. There was no significant 

difference between the duration of looks toward faces signs and travel-time signs.

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Figure 48. Chart. Number of glances at each CMS that received at least one look.

Figure 49. Chart. Predicted mean duration of individual looks as a function of sign content. 

Fixation Results

GEE were used to model the probability of a participant fixating on each CMS. A binomial 

response distribution and logit link function were assumed. The predictor was the sign content. 

Sign content was a significant predictor of fixation probability, 2

(2) = 84.30, p < 0.001. Post

hoc comparisons indicated that the probability of fixating on a blank sign was significantly less 

than the probability of fixating on faces or travel-time signs and that the difference between the 

probabilities of fixating on travel-time and faces signs was not significant. Figure 50 shows the 

probability of fixating on individual CMSs as a function of their content.

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Figure 50. Chart. Expected mean probability of a fixation on a CMS as a function of sign 

content.

GEE models were used to model the number of fixations on the CMSs. A Poisson response 

distribution and log link function were assumed, and sign content was the predictor. Figure 51

shows the predicted mean number of fixations as a function of sign content. Sign content was a 

significant predictor, 2

(2) = 35.90, p < 0.001. Post hoc comparisons indicated all three predicted 

means were significantly different from each other. 

Figure 51. Chart. Predicted mean number of fixations across all signs as a function of sign 

content.

Mean fixation duration was examined for those signs that received at least one fixation. GEE

models were used to evaluate fixation duration. A gamma response distribution and identity link 

function was assumed. Sign content was the predictor variable. Sign content significantly 

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predicted fixation duration, 2

(2) = 15.10, p = 0.001. Post hoc comparisons indicated mean 

fixations on travel-time signs were significantly longer than those on blank or faces signs, and 

the latter two were not significantly different from each other. Figure 52 shows expected mean 

fixation durations for blank, travel-time, and faces signs along with the 95-percent confidence 

limits for those means.

Figure 52. Chart. Expected mean fixation duration as a function of sign content.

The average fixation duration was 0.5 s or less. However, the average duration may not reflect 

the existence of long fixations on CMSs that might represent an unsafe driver distraction. For 

in-vehicle device distraction, it is suggested that display devices that capture visual attention for 

more than 2 s represent a safety risk.(55,56) Although no such guidance is available for display 

devices located above the roadway, it is doubtful whether displacement of gaze only a few 

degrees above the forward roadway would be unsafe. However, should generalization of the 2-s 

capture rule be considered appropriate, then a few unsafe fixations were observed. Table 26

shows that 1.8 percent of fixations on the simulated CMSs in this study were greater than 2 s. 

Table 27 shows that more than 50 percent of the durations greater than 2 s were for travel-time 

messages.

Table 26. Distribution of fixation durations.

Duration Frequency Percentage

Less than 1 s 1,629 90.95

Between 1 and 1.5 s 101 5.64

Between 1.5 and 2 s 28 1.56

Greater than 2 s 33 1.84

Total 1,791 100.00

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Table 27. Frequency of fixations greater than 2 s.

Sign 

Category

Frequency of Fixations 

> 2 s Duration

Blank 6

Faces 8

Time 19

In chapters 5 and 6, beginning and end of glances were examined. It was found that glances 

began at about the same distance regardless of sign content and that glances at faces signs ended 

closer to the sign than did glances at blank or travel time signs. Similarly, mean fixation distance 

was examined. The overall test and post hoc tests were all GEE models, with sign content as the 

predictor for mean fixation distance. Mean fixation distance was assumed to be gamma 

distributed, and an identify link function was used. The main effect of sign content was 

significant, 2 (2) = 23.01, p < 0.001. The mean fixation distance results are in line with those for 

glances: expected mean distance was greatest for blank signs and least for faces signs, with 

travel-time signs falling between. Post hoc comparisons showed all three expected means, as 

shown in figure 53 are significantly different from each other.

1 ft = 0.305 m

Figure 53. Chart. Expected mean distance of fixations on a CMS as a function of sign 

content.

Driving Performance Measure

Travel speed was analyzed with a GEE model that assumed a gamma distribution and identity 

link function. The sign content of the data collection zone and location of the logs served as 

predictor variables. The travel speed dependent measure was based on mean travel speed for 

each data collection zone for each participant. Data collection zones 4 and 72 (the zones with 

logs in the roadway) were excluded from the analysis. Figure 54 shows the resulting expected 

mean speeds as a function of the content on the CMS and log location. The interaction of log 

location and sign content was significant, 2

(2) =8.94. p = 0.011. This unexpected interaction 

χ

0

100

200

300

400

500

600

700

Blank Travel Time Faces

Expected Mean 

Fixation 

Distance (ft)

Sign Content

χ
83

resulted because the group that encountered the logs late in the trip exhibited a nearly constant 

speed regardless of CMS content, whereas the group the encountered the spilled logs early in the 

trip drove significantly slower than average when approaching blank signs and significantly 

faster than average when approaching faces signs. Also striking is that all groups drove below 

the instructed and posted speed in all three data collection zone types.

No predictor variable (sign content, log location, or data collection zone order) was significantly 

related to speed variability. 

1 mi/h = 1.6 km/h

Figure 54. Chart. Expected mean speed as a function of sign content.

Faces Recognition

The d' measure from signal detection theory was used to assess the ability of participants to 

distinguish between pictures they had been exposed to during the drive and similar pictures not 

previously displayed.(57,58) The obtained estimate was quiet low, mean 

d' = 0.25, SD (standard deviation) = 0.37. A d' of zero would indicate no ability to distinguish 

between targets and foils. This finding indicates that participants showed very little ability to 

distinguish the new pictures from the ones displayed during the drive. 

DISCUSSION

Distraction From Monitoring the Road Ahead

A total of 59 of 80 drivers were successful in detecting and avoiding the spilled load of logs. The 

detection task was difficult enough that many of the remaining 21 drivers struck the logs because 

they were indecisive in reacting, such as deciding to seek a gap in the adjoining lanes and then 

failing to find that gap in time to avoid the logs. A few drivers showed no indication of detecting 

the logs. In any case, failure to detect or react successfully to the threat did not appear to vary 

with the content of the sign that was being approached. The log avoidance measure yielded no 
84

evidence that driving-irrelevant stimuli, even stimuli considered highly salient in other contexts, 

would distract drivers from their primary (driving) task more than would a blank overhead sign. 

Eye Gaze Distraction Evidence

The eye gaze data from all three metrics (glance, look, and fixation) converged on the same 

general conclusion: drivers were about equally likely to shift their visual attention to travel-time 

messages as they were to colorful, changing, and driving-irrelevant content (e.g., faces), and less

likely to shift visual attention to a blank CMS than to a sign with information content.

There were few fixations on the CMSs longer than 2 s, and the majority of these were on traveltime signs. It was thought that faces of celebrities or faces displaying strong emotions might 

attract attention more than predictably structured text messages. To enhance the saliency of the 

faces, they changed every 3 s. Nonetheless, the faces did not attract gaze more than travel-time 

messages. 

Recognition of Faces 

The finding that pictures not shown before could not be reliably distinguished from pictures that 

were repeatedly presented on the CMS may suggest that the foils chosen for use in the test were 

too similar to the targets. However, the findings also suggest that participants did not devote 

much attention to studying the pictures on the overhead signs (and the gaze data would seem to 

confirm this). Because the participants were not told that recognition of the face pictures would 

be tested, there was no motivation for them to study the pictures beyond whatever intrinsic value 

the pictures might hold. A stronger test of the ability of drivers to avoid distraction from the 

primary task—driving—might be to offer some incentive to drivers to attend to the CMS 

messages. The challenge of a study with incentives to attend to the CMSs would be to avoid 

making the driving task secondary because of unrealistic contingencies.

SUMMARY AND CONCLUSIONS

These findings suggest that messaging on CMSs that is not related to driving would be no more 

distracting than traffic-related messaging or blank signs. As in the previous experiment, these 

results apply to a single relatively long trip. Should drivers habituate to frequently occurring 

CMSs, then any distraction away from detecting road hazards should be less.

The findings indicated that when a message is displayed on a CMS, drivers move their center of 

gaze to the sign about 50 percent of the time. When the CMS is blank, drivers still have a 

25-percent probability of shifting their gaze to it. These gazes are generally short, about 0.5 s. 

The number of short glances is generally small, with an average of one fixation per sign with 

content.

The aims of this and the experiment reported in chapter 5 were limited to a strong test of whether 

travel-irrelevant content on CMSs would distract drivers. The answer is that under the conditions 

tested, irrelevant messaging, even if designed to be salient in other settings, would not be more 

distracting than traffic-related messaging, which itself did not appear to stress the visual attention 

capacity of drivers.
85

The present tests did not present the CMSs in environments with high amounts of visual clutter, 

as might be present in an urban environment with tall buildings, billboards, and overpasses, in 

addition to other critical highway signs. The tests simulated daytime conditions with signs that 

were not brighter than static signs or the simulated sky. The contrast ratio of white text to the 

black background, measured in a previous study in the simulator, was 14.6. This ratio is 

somewhat greater than that recommended by the MUTCD (8 to 12) (see section 2L.04, 

paragraph 11) but much less than contrast ratio of the CMS reported in chapter 3.

(2) Aside from 

the logs in the road in the experiment reported in this chapter, eye gaze behavior was not 

assessed in areas with hazardous driving conditions. The participants in these experiments were 

not young novice drivers. For these and other reasons, it is possible that in some conditions, with 

some driver populations, some CMS content might be distracting and pose a safety risk. These 

experiments suggest that it would be challenging to present visual information that would 

compel drivers to shift their attention from the primary driving task. 

87

CHAPTER 7. THE EFFECT OF FREQUENCY AND SPACING OF GUIDE SIGNS ON 

DRIVER BEHAVIOR

This study examined the effects of the frequency, spacing, and information content of guide 

signs on driver performance. Specifically, it looked at the effects of the number and spacing of 

supplemental guide signs and specific-service logo guide signs.

Highway agencies (e.g., State departments of transportation and toll authorities) are sometimes 

under pressure to post more supplemental guide signs than is permitted in the MUTCD.(2) For 

instance, when a college or university is listed on a supplemental sign, other higher education 

institutions near the same interchange may request similar acknowledgment. The demand for 

listing on specific-service signs (i.e., signs for gas, food, lodging, camping, attractions, and 24-h 

pharmacies) may also exceed available sign locations that conform to MUTCD requirements. 

Supplemental guide signs provide information regarding destinations accessible from an 

interchange other than places displayed on the standard interchange signing. Like standard 

interchange guide signs, supplemental guide signs have white lettering on a green background. 

The MUTCD limits supplemental guide signs to one per interchange and a supplemental sign 

may list no more than two destinations.(2) The manual also specifies that there should be at least 

800 ft (244 m) between a supplemental sign and other guide signs but cautions that the 

supplemental sign may overload drivers’ ability to process information.

Specific-service guide signs have white lettering on a blue background. The MUTCD 

recommends that these signs be spaced a minimum of 800 ft (244 m) apart from each other and 

from other guide signs.(2) The services on a specific-service sign may be represented by either 

text or a logo, with a maximum of six services per sign. A maximum of four specific-service

signs may serve an interchange. The limitation on the number of guide signs, the distance 

between them, and the amount information on individual signs is intended to avoid overloading 

drivers’ ability to receive information and to make appropriate decisions.(2)

Although it seems reasonable that too much signing could decrease the effectiveness of 

navigation-related signing, there is little empirical evidence that this is the case, and no 

empirically based literature is available that would enable quantification of what is too much. 

NCHRP Report 488: Additional Investigations on Driver Information Overload presents a model 

for quantifying the relative information load of sign arrays along a highway segment but does not 

provide a method for determining the absolute level at which too much information is present.(20)

NCHRP 488 describes driver information overload and its potential consequences as follows:

(20)

“Driver information overload” is defined as providing a motorist with too much 

information, through a series of devices or conditions, for a driver to have 

adequate time to perceive and respond properly. Therefore, the information load 

on a driver is a property not only of the specific sign he or she is encountering, but 

also of the roadway context in which the sign occurs, the information context in 

which the sign occurs, characteristics of the driver, and the particular navigational 

task. Where drivers are confronted with more information than they can process, 

they may decelerate severely or drive unduly slowly, make late or erratic 
88

maneuvers, take an improper route alternative, ignore critical information, fail to 

monitor other traffic, or have excessive eyes-off-the-road time episodes. These 

behaviors have obvious safety and operational consequences. (p. 1)

A key point is that the information load from guide signs is not a property of the signs 

themselves but rather depends on the driver’s navigation task and the context in which the sign is 

encountered. Given that the driver requires navigation information, overload may occur if the 

driver cannot readily obtain, in the time available, the needed information on a sign or cannot 

determine that the needed information is not on that sign. The time available depends on the 

demands of traffic, roadway geometry, and the number of other signs that the driver needs to 

monitor. Drivers on a familiar route would not need navigation sign information and should not 

experience information overload attributable to guide signs. As long as other signs near the 

roadway, such as billboards, do not resemble highway signs, guide sign information overload

should not occur.

(59) In addition, because guide signs are color coded (i.e., blue background for 

specific-service signs and green background for other interchange and supplemental guide signs),

drivers who need only one type of information (i.e., either services or destination guidance) 

should not be expected to be overloaded by the presence of both types of signing. Furthermore, 

legends on specific-service signs (e.g., lodging, gas) are intended to relieve the driver of 

searching all specific-service signs for a specific type of service. The use of color coding and 

service legends assumes drivers use this information to reduce the load imposed on them. With 

respect to specific-service sign legends, this experiment provided a test of that assumption.

Drivers who need navigation information may have insufficient time to acquire the information 

in the context of too many signs insufficiently spaced if the legibility distance is insufficient, if it 

is unclear which signs to attend to, or if the roadway and traffic demands are high.

Given the potential consequences of information overload, quantification of what is too much 

could be useful in guiding and supporting rule-making and guiding agencies that perceive a need 

to post guidance information that goes beyond current sign frequency and spacing guidelines and 

regulations.

The origin of the FHWA requirement for an 800-ft (244-m) minimum spacing between freeway 

guide signs is unclear. The first interstate freeway signing and marking manual, published by 

American Association of State Highway Officials (now AASHTO) in 1958, includes the 

provision that “in no case shall guide signs...be spaced closer together than 800 feet.”(60)

Although the origin of the 800-ft (244-m) minimum is unclear, there is supporting justification 

for a distance of about that magnitude. One assumption behind the minimum distance between 

signs is that the most vulnerable (inexperienced and elderly) drivers can process only one sign at 

a time and the minimum spacing ensures that vulnerable drivers will not be simultaneously 

confronted with two signs that must be read. Another is that each sign must be available for a 

minimum amount of time so that the driver can time-share sign reading with other driving 

demands. Mace, Hostetter, and Seguin conducted a series of laboratory and on-road experiments 

and concluded that any exposure time of more than 2 s is adequate for processing highway guide 

signs.(59) Their subjects were not vulnerable road users so a minimum greater than 2 s is probably 

reasonable. Assuming a travel speed of 65 mi/h (104 km/h), 2 s would represent 190 ft (58 m) of 

travel distance. With 10-inch- (25-cm-) high letters, the minimum height for the smallest 

lettering on freeway signs, the legibility distance would be 300 ft (91 m) with an assumed
89

minimum visual acuity of 20/40. Having read a guide sign and determining that a lane change 

maneuver is required, a maneuvering distance must be accounted for. The 18-inch (46-cm) letter 

height for critical freeway guide sign information yields a minimum legibility distance of 540 ft 

(165 m), and a maximum legibility distance of 1,080 ft (376 m). For a driver with 20/20 vision 

signs would need to be about 1,000 ft (305 m) apart to guarantee two guide signs would not be 

legible at the same time. Whether the presence of two legible guide signs less than 8.4 s apart 

(800 ft (244 m) divided by 95 ft/s (29 m/s) confronts drivers with a time-sharing challenge (i.e.,

information overload) is an empirical question that has not been adequately addressed.

The purpose of this study was to explore the effect of varying numbers of guide signs and their 

spacing on driver performance. The effects of the following variables were addressed:

• Sign spacing of drivers’ performance in obtaining and using guide sign information.

• Number of destinations listed on guide signs on driver performance.

• Sign spacing on driver performance.

• Guide sign frequency on visual scanning of the road ahead.

The study examined driver performance in the case where the driver had information needs that 

relied on guide and specific-service signs.

METHODS

Participants in this study drove on a simulated freeway with four lanes in their direction of travel. 

They were instructed to take freeway exits for Holt Avenue, Harvard University, and the Holiday 

Inn®. This instruction was intended to require participants to monitor all three types of signing:

interchange guide signs, supplemental guide signs, and specific service signs. The freeway had 

21 entrances and 22 exits that comprised 21 unique freeway segments, where a segment 

extended from exit gore to exit gore. Participants were not informed of how many exits were 

signed for the assigned destinations. There was one of each. The number of specific service signs 

per segment varied between zero and three. The number of specific service signs also varied 

from zero to three. There were always two advance guide signs per segment, but some exits had 

two street name destinations per sign while the remainder listed only one street name. The 

number of street names on the exit for Holt Avenue was a between-group variable with two 

levels (one or two street names). The number of supplemental guide signs per exit varied 

between one and three and was a with-group variable. The number of destinations per sign varied 

from one to two. The number of supplemental guide signs and the number of destinations at the 

exit for Harvard varied among subjects.

Dependent measures were the probability of taking correct and incorrect exits, the frequency and 

duration of various eye-glance measures, and speed and lane keeping in the proximity of 

guide signs.

The Simulator

The experiment was conducted in the FHWA highway driving simulator. In the simulator 

experiments described earlier in this report, the simulator’s out-of-vehicle display consisted of a 

horizontal projection of a 240-degree field of view onto cylindrical screen. However, at the time 
90

of this study, the projectors had exceeded their life expectancy and were no longer capable of 

achieving the necessary brightness or resolution to support a sign study. Because the projectors 

could not be replaced within the timeframe of this study, three high-resolution LCD monitors 

were used to display the forward 104 degrees of the field of view. Figure 55 shows the view of 

the roadway on the LCD monitors. (The image on the monitors is from a subsequent study.) 

Two of the original projectors were used to complete the side portions of the 240-degree

horizontal display. Each of the LCDs was 30 inches (0.76 m) on the diagonal with a 16:10 aspect 

ratio. The LCD monitors’ resolution was 2,560 horizontal pixels by 1,600 vertical pixels. LCD 

brightness was approximately 108 fl (370 cd/m2

) with a typical contrast ratio of 1,000:1. The 

distance of the monitors from the driver’s eye point varied with driver height and seat position. 

The nominal distance of the center monitor was 36 inches (0.9 m). The right and left monitors 

were 39 and 49 inches (1 and 1.2 m) from the design eye point, respectively. All distance 

measurements were to the center of the respective displays. Images on each display were scaled 

to present a 1:1 correspondence with the real-world equivalents of the virtual world. All displays 

refreshed at 60 Hz. The minimum pixel response time on the LCD was 8 ms.

Figure 55. Photo. View of the three LCD monitors from slightly behind the driver’s eye 

point.

Because the LCDs were relatively close to the driver’s eye point, the simulated horizon height 

was adjusted to account for eye-point height differences of individual participants. 

The LCD monitors were mounted in the windshield area of the sedan in the simulator. This 

placement required removal of the windshield. The simulator’s motion base was not enabled in 

this experiment. The car’s instrument panel, steering, brake, and accelerator pedal all functioned 

in a manner similar to real-world compact cars. Rear view mirrors were simulated using 

7.8-inch-wide (20 cm) by 4.8-inch-high (12 cm) color LCD with 800 pixels horizontally and 

480 pixels vertically. These displays had a contrast ratio of 400:1. Left and right outside 

simulated mirrors were mounted over the sedan’s original outside mirrors. The center-mounted 

rear view LCD was placed as near as possible to the location of the vehicle’s original mirror. 
91

The simulated vehicle was equipped with a hidden intercom system that enabled 

communications between the participant and a researcher who ran the experiment from a remote 

control room. The researcher in the control room could also view the face video from the eyetracking system and thereby monitor the participant’s wellbeing.

The Simulation Scenario

Seven signing conditions were simulated across 21 freeway segments, where each segment had 

one entrance merge and one exit. Table 28 summarizes the key attributes of each condition. All 

conditions had three interchange guide signs; a 1-mi (1.6-km) advance sign, a 0.5-mi (0.8-km)

advance sign, and a sign at the exit gore. Specific-service signs always preceded the 1-mi 

(1.6-km) advance guide sign. The distances between each specific-service sign and the third 

specific-service sign with the 1-mi (1.6-km) advance sign are shown in the column labeled 

“Distance Between Signs” in table 28. With one exception, the supplemental guide signs were 

placed between the 1-mi (1.6-km) advance sign and the 0.5-mi (0.8-km) advance sign. In 

condition 3, one supplemental sign followed the 1-mi (1.6 km) advance sign by 800 ft (244 m),

and one supplemental sign followed the 0.5-mi advance sign by 800 ft (244 m). The distances 

between the 1-mi (1.6 km) advance sign and the other supplemental guide signs is specified in 

the “Distance Between Signs” column of table 28. The number of destinations applies to both the 

advance guide signs and the supplemental guide signs. Each destination appeared once across the 

21 segments.

Table 28. Summary of the seven signing conditions.

Condition

Advance 

Guide Signs

Supplemental 

Guide Signs

SpecificService SignsNumber of 

Destinations

Distance 

Between 

Signs (ft)

1 3 0 0 1 2,640

2 3 1 1 1 800

3 3 1 1 2 800

4 3 2 1 1 800

5 3 2 2 2 800

6 3 3 3 2 400

7 3 3 3 2 200

1 ft = 0.305 m

The Holt Avenue exit was always on one of the first seven segments, the Harvard University exit 

was always on one of the second series of seven segments (i.e., 8 through 14), and the Holiday 

Inn® exit was always on one of the final seven exits. There were seven different orders in which 

the seven signing conditions were presented, and the seven target exits occurred equally often in 

each condition. With the exception of 2 participants, who were inadvertently run in the wrong 

conditions, each of the 49 combinations of targeted exits with order of conditions was presented 

to 2 participants. 

Guide sign lettering used the FHWA series E (modified) font with nominal 18-inch (46 cm)

letter height. To achieve the appropriate legibility distance (800 to 1,000 ft (244 to 305 m) for the 

signs, all signs were oversized by a factor of 1.5. This resulted in letters subtending a visual 

angle equivalent to a 27-inch- (68-cm-) high lettering. The street names on the advance guide 
92

signs were all interchange street names from I-10 between Los Angeles, CA, and Ontario, CA. 

The destinations of the supplemental guide signs consisted of colleges, museums, and other 

destinations with no correspondence to actual geography (e.g., Cal Tech and the University of 

Virginia appeared on the same supplemental sign).

All specific-service signs displayed six logos. The logos on the specific-service signs, which 

were also oversized, were business logos used on signs on I-95 in Virginia. More than 30 unique 

logos were used. The MUTCD specifies that where lodging, food, and gas services are displayed, 

the signs are sequenced in that order.(2) However, in this experiment when more than one service 

sign was present, the lodging services sign was always preceded by a food services sign. The 

reversal in the normal order was intended to make the search task more challenging. It was 

assumed that most motorists are unaware of the order specified by the MUTCD for service signs. 

The business logos on the signs varied from exit to exit as did the position of individual logos 

that appeared on multiple signs. The target logo (i.e., Holiday Inn®) was always in the center of 

the lower row on the lodging sign. Nominally, condition 1 did not include specific-service or 

supplemental guide signs. However, as was done for all participants, the research design called 

for condition 1 participants to take an exit signed for the Holiday Inn® and Harvard University. 

Therefore, for condition 1 participants, a lodging or supplemental guide sign was added at the 

intended exits so that all participants in all conditions would correctly take three exits if they 

made no navigation errors.

To make the driving workload somewhat realistic, a traffic simulation model was used to 

generate traffic. Although the same traffic was generated for all participants (the traffic model 

random number seed was constant), the location of individual participants in the traffic stream 

could and did vary depending on how fast the participant drove and in which lanes he or she

traveled. Traffic counters were located between each exit, and the number of vehicles crossing a 

counter while the participant was between the respective exits was recorded. Table 29 shows the 

resulting statistics for vehicles per lane per hour during the time a participant was somewhere in 

the respective zones. Software failures resulted in some data loss as indicated by the Number of 

Participants column, which shows the number of participants contributing to each zone statistic.
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Table 29. Vehicles/lane/h summary statistics for each data collection zone.

Zone

Number of 

Participants Mean

Standard 

Deviation Minimum Maximum

1 97 963 222 72 1,213

2 97 985 215 99 1,227

3 96 1,012 201 197 1,223

4 96 1,024 193 111 1,214

5 96 1,050 170 365 1,269

6 96 1,060 170 344 1,289

7 96 1,069 151 364 1,272

8 96 1,077 142 411 1,256

9 96 1,077 138 372 1,357

10 96 1,085 128 423 1,283

11 96 1,091 127 414 1,234

12 96 1,084 132 359 1,311

13 96 1,078 126 382 1,284

14 96 1,073 121 427 1,273

15 96 1,079 131 424 1,411

16 96 1,082 127 400 1,320

17 96 1,070 133 376 1,320

18 96 1,062 131 425 1,301

19 96 1,064 142 368 1,292

20 96 1,049 154 383 1,289

21 50 953 239 423 1,453

The simulated traffic did not enter or exit the freeway at any point. However, the simulated 

vehicles did change lanes and execute passing maneuvers. Participants who drove faster than 

65 mi/h (105 km/h) and attempted to closely follow other vehicles encountered more traffic than 

participants who maintained 65 mi/h (105 km/h) or less. 

Table 30 shows the percent of vehicles in each lane for which each of four desired speeds was 

specified. The minimum desired speed in the right lane was set at 70 mi/h (113 km/h) so that

participants who were instructed to maintain 65 mi/h (105 km/h) would not have an incentive to 

leave the right lane.

Table 30. Speed parameters (percent vehicles per speed bin per lane) for traffic control by 

the traffic simulation model.

Lane

Speed (Mi/h)

65 70 75 80

Lane 1 (right) 0 50 25 25

Lane 2 10 70 20 0

Lane 3 0 25 71 4

Lane 4 (left) 0 0 95 5

1 mi/h = 1.6 km/h
94

Eye-Tracking System

The same eye-tracking system was used in this experiment as in the previous simulator 

experiments in this report. The mean (M) accuracy of left-eye gaze-position across participants 

was 1.3 degrees (radius) with an SD of 0.65 degrees (95th percentile accuracy M = 2.3, 

SD = 1.2). The mean accuracy of right-eye gaze-position across participants was 1.6 degrees

(radius) with an SD of 0.83 degrees (95th percentile accuracy M = 2.9, SD = 1.9). The eyetracking data (e.g., gaze direction of each eye, head position, etc.) were merged with data from 

the simulator (e.g., vehicle speed, lane position, and steering wheel position) and the current 

forward view of the simulation visual scene (approximately 39 degrees horizontal by 24 degrees

vertical). The merge was accomplished using a MAPPS® scene recorder.(61)

To quantify when and for how long participants looked at each guide sign, a researcher used 

analysis software to indicate an ROI on individual frames of the recorded video image. An

example of an analyst’s screen with a coded ROI for specific-service signs 400 ft (122 m) apart 

is shown in figure 56. Also shown is the road ahead ROI. When three specific-service signs were 

200 ft (61 m) apart, their ROI overlapped with ROIs for the following advance guide sign and 

supplemental guide sign. Where ROIs overlapped, a set of priorities determined which ROI was 

credited to a gaze vector that landed on the overlap. The order of priorities, from highest to 

lowest, was specific service, supplemental, advance, and road ahead. These priorities had the 

greatest effect in condition 7, where the signs were 200 ft (61 m) apart. Only conditions 6 and 7 

had ROIs that included more than one sign, and only signs that were 200 ft (61 m) or 400 ft 

(122 m) apart. 

Each ROI included the sign and approximately 8 ft (2.4 m) (virtual) to each side of the sign. 

Each ROI was coded for 10 s on the approach to the sign and terminated when the sign began to 

pass out of the forward 39-degree field of view (i.e., was more than 19.5 degrees to the right 

driver’s forward view). For ROIs that included more than three signs, the coding began 10 s 

before reaching the first of the signs and ended when the last sign passed out of the field of view. 

Figure 57 shows an ROI for a specific-service sign, and figure 58 shows an ROI for an advance 

guide sign.
95

Figure 56. Screen capture. Analyst’s screen for coding shows ROI for specific-service signs 

that are 400 ft (122 m) apart.

Figure 57. Screen capture. ROI drawn around a specific-service sign.
96

Figure 58. Screen capture. ROI drawn around an advance guide sign.

Participants

Participants were recruited from a list of volunteers maintained by FHWA’s Safety Research and 

Development Human Factors Team. Of 127 individuals recruited, 113 completed driving the 

entire scenario. Of the 14 individuals who did not complete the drive, 2 dropped out because of 

simulator sickness, 2 elderly people had trouble with instructions or controlling the vehicle, 

8 persons either could not be calibrated for eye tracking or otherwise did not track well, and with 

2 persons, the simulator hardware or software failed.

Of the 113 participants who completed the drive, 98 provided usable eye-tracking data. During 

recruitment, an attempt was made to balance the sample with respect to age and gender. Because

the median age in the participant database was 46 years, the goal was to recruit equal numbers of 

male and female drivers over 46 and 46 years of age or younger. The mean age of the 

98 participants with complete data was 44 years (range 18 to 76 years), with 49 females and 

49 males. 

Instructions

The instructions to participants were as follows:

You will be driving on a freeway that has four lanes in the direction you are 

traveling. There will be other vehicles on the road, although traffic should not be 

heavy. You should obey the law and drive as you normally would. You can drive 

in any lane you choose. The posted speed limit is 65 miles per hour. Your task is 

to watch for three destinations: Holt Ave, Harvard University, and the Holiday 

Inn. When you see the exit for any of these destinations, take that exit. We will be 

recording how you drive and where you look. Please drive as you normally 
97

would. If you travel close to 65 MPH and take all the correct exits, you will 

receive a $10 bonus. Do you have any questions? The trip should take about 

45 min.

Remember, you are looking for:

• Holt Ave

• Harvard University

• The Holiday Inn

Participants were also handed a picture of the Holiday Inn® logo so it could be assumed that the 

target logo was familiar.

Before beginning the test drive, participants completed a practice drive. The practice drive was 

similar to the test drive except that there was no traffic and there were only three exits. The exits 

on the practice drive were signed for the Holiday Inn®, Harvard University, and Holt Avenue 

and were presented in that order. Each of the practice exits had three guide signs, one specificservice sign, and one supplemental guide sign with spacing compliant with the MUTCD

guideline.

(2)

Participants received $40/h compensation for a typical 1.5 h of participation. In addition, the 

$10 bonus mentioned in the instructions was paid when appropriate.

RESULTS

Throughout this report, error bars in the charts and graphs represent 95-percent confidence limits 

around the means.

Exit Taking

Table 31 shows the percent of participant results for the first seven exits, one of which contained 

advance guide signs with the Holt Avenue destination. Recall that each condition was 

represented once among the first seven segments. Therefore, for each condition, a correct 

response is indicated on the positive diagonal (i.e., no/no and yes/yes cells), if guide signs did not 

include Holt Avenue, then the correct response was not to exit (no/no), and if the guide signs 

listed Holt Avenue, then the correct response was to exit (yes/yes). If the participant took an exit 

that did not have Holt Avenue as a destination, then that participant is represented in the upperright cell for that condition (no/yes), and if the participant failed to take the Holt Avenue exit,

then that participant is represented in the lower-left cell for that condition.
98

Table 31. Percent of participants on segments 1–7 who responded correctly to guide signs 

(no/no and yes/yes) and false alarms (no/yes) and misses (yes/no) as a function of signing 

condition.

Condition

Holt Exit

Present

Exited? 

No

(percent)

Exited?

Yes

(percent)

1 No 85.7 0

Yes 1.0 13.3

2 No 83.7 3.1

Yes 0 13.3

3 No 85.7 0

Yes 0 14.3

4 No 84.7 1.0

Yes 0 14.3

5 No 85.7 0

Yes 1.0 13.3

6 No 84.7 0

Yes 0 15.3

7 No 84.7 1.0

Yes 2.0 12.2

Table 32 shows the analogous results for segments 8–14, where the correct exit had a 

supplemental guide sign that listed Harvard University as a destination. Table 33 summarizes 

exit-taking performance for segments 15–21, where the correct exit had a supplemental guide 

sign with the Holiday Inn® logo.
99

Table 32. Percent of participants on segments 8–14 who responded correctly to the 

supplemental guide signs (no/no and yes/yes) and false alarms (no/yes) and misses (yes/no) 

as a function of signing condition.

Condition

Harvard 

Exit

Present

Exited? 

No

(percent)

Exited?

Yes

(percent)

1 No 84.7 1.0

Yes 0 14.3

2 No 86.7 0

Yes 0 13.3

3 No 85.7 0

Yes 0 14.3

4 No 84.7 1.0

Yes 2.0 12.2

5 No 84.7 1.0

Yes 1.0 13.3

6 No 84.7 0

Yes 1.0 14.3

7 No 85.7 0

Yes 3.1 11.2

Table 33. Percent of participants on segments 15–21 who responded correctly to the 

specific-service signs (no/no and yes/yes) and false alarms (no/yes) and misses (yes/no) as a 

function of signing condition.

Condition

Holiday 

Inn® Exit

Present

Exited? 

No

(percent)

Exited?

Yes

(percent)

1 No 85.7 0

Yes 1.0 13.3

2 No 86.7 0

Yes 0 13.3

3 No 85.7 0

Yes 1.0 13.3

4 No 84.7 1.0

Yes 1.0 13.3

5 No 85.7 0

Yes 0 14.3

6 No 84.7 0

Yes 0 14.3

7 No 85.7 0

Yes 1.0 13.3

There were very few exit-taking errors, and the errors that were observed did not appear to be 

associated with a type of sign (i.e., street name, supplemental destination, and specific service) or 

with any of the variables associated with the seven signing conditions (i.e., sign spacing, number 
100

of signs, or number of destinations on the signs). It is important to remember that the participants 

were not informed which exits might have which target destination and therefore were expected 

to search for all 3 targets at all 21 exits. The distinction between the three sets of seven exits is 

made so that if there were a difference in the detection rate of a particular type of target, that 

could be seen. Also it is evident that the target detection rate was stable across the 21 exits.

The research design was not factorial with respect to the number of signs, the number of 

destinations on a sign, or the distance between signs. Therefore, pairwise comparisons were used 

to test for significant differences with respect to individual factors and interactions between 

factors. For instance, to test whether the distance between signs affected correct exit taking, 

McNemar’s Test was performed on the data in table 34. The No/No cell is empty because no 

participant made an exit error in both condition 6 (400 ft (122 m) between signs) and condition 7 

(200 ft (61 m) between signs). One participant (1.02 percent) made no errors in condition 7 and 

at least one error in condition 6. Six participants made at least one error in condition 7 but none 

in condition 6. Ninety-one participants made no errors in either condition 6 or 7. Although there 

were more errors with 200-ft (61-m) sign separation than with 400-ft (122-m) separation, this 

difference is small and not statistically significant at the 0.05 level.

Table 34. Percent of participants who responded in either conditions 6 or 7 or both 6 and 7.

Condition 6

Condition 7

No Yes

No 0 1.02

Yes 6.12 92.86

In all cases, McNemar’s Test assuming a binomial distribution was used.(62) No significant 

differences in correct exit-taking behavior were observed as a function any of the three factors or 

their interactions.

Gaze Behavior

Only glance and fixation measures were used in the analysis of gaze behavior in this study. 

Looks were not addressed for two reasons: (1) in the previous experiment, the look measure led 

to conclusions similar to those for the glance and fixation measures, and (2) where gaze vectors 

fell near the border between ROIs, it was difficult to determine where one look ended and 

another began because the vector may frequently shift back and forth across the border between 

ROIs.

Glance Results

As in the previous simulator experiments, a glance was defined as any accumulation of 

12 (12 × 0.008 s = 100 ms) or more hits on an ROI, where a hit is a single 120-Hz center of gaze 

vector estimate that falls on the sign’s ROI. 

Table 35 shows the probability of a glance to sign ROIs. Where there was more than one ROI for 

a sign type (e.g., first and second advance guide signs and the (third) exit guide sign), the 

probabilities for each ROI are shown. Cells are marked “N/A” if there was no second or third 

ROI. In conditions 5 and 6, there were three specific-service signs and three supplemental guide 
101

signs, but only one ROI that cover all three signs of the respective types. The probability of 

glancing at all unique signs was high and never less than 0.86. The probability of glancing at 

redundant second and third guide signs tended to be lower than for other signs but was never less 

than 0.74.

Table 35. Probability of glance at signs as a function of condition and sign type.

Condition Sign Type

Probability of Glance by 

Order of Appearance

First Second Third

1

Guide 0.88 0.87 0.76

Supplemental guide 0.93 N/A N/A

Specific service 0.86 N/A N/A

2

Guide 0.89 0.89 0.74

Supplemental guide 0.89 N/A N/A

Specific service 0.97 N/A N/A

3

Guide 0.93 0.92 0.85

Supplemental guide 0.94 N/A N/A

Specific service 0.98 N/A N/A

4

Guide 0.88 0.83 0.75

Supplemental guide 0.86 0.85 N/A

Specific service 0.96 N/A N/A

5

Guide 0.91 0.86 0.86

Supplemental guide 0.94 0.96 N/A

Specific service 0.94 0.96 N/A

6

Guide 0.95 0.92 0.80

Supplemental guide1 0.99 N/A N/A

Specific service1 1.00 N/A N/A

7

Guide 0.92 0.90 0.85

Supplemental Guide1 0.99 N/A N/A

Specific Service1 0.99 N/A N/A

N/A= Not applicable. No second or third ROI.

1

One ROI included three consecutive signs.

Although the two advance guide signs and the guide sign at the exit contained redundant 

information, drivers tended to glance at each sign regardless of glances at the previous sign.

Table 36 shows the conditional probability of glances at a second advance guide sign given that 

the first sign received a glance and the probability of a glance at an exit guide sign given a glance 

at the preceding (0.5 mi (0.8 km) advance guide sign.
102

Table 36. Probability of glancing at a subsequent guide sign given a glance at the preceding 

advance guide sign.

Condition

Probability (Glance 

to Second Given 

Glance to First)

Probability (Glance 

to Third Given 

Glance to Second)

1 0.91 0.82

2 0.92 0.79

3 0.95 0.87

4 0.89 0.81

5 0.9 0.92

6 0.92 0.83

7 0.91 0.9

The mean duration of glances to sign ROIs was calculated two ways. First, the average duration 

of glances was computed for all ROI instances where zero duration (actually 0.00001 s) was 

recorded when drivers did not glance at an ROI. This method provided an estimate of the total 

attention a sign might attract and is shown in table 37. Cells in table 37 marked “N/A” represent 

locations where signs did not exist. Second, the average durations of glances at an ROI was 

computed only for actual glances (i.e., mean duration of a glance given a glance took place). 

These means are shown in table 38. Recall that glance durations are the sum of all 0.0083-s hits 

of a gaze vector on an ROI and may include multiple fixations interspersed with glances 

elsewhere. Specific service-sign ROIs captured considerably longer glances, more than 3 s in all 

conditions except for the gas sign in condition 5, which still had an average glance duration of 

more than 2 s. In condition 6, three specific-service signs, 400 ft (122 m) apart, were included in 

one ROI; that ROI had a mean glance duration of 7.4 s. Assuming a speed of 95 ft/s (20 m/s), the 

ROI was present for 18 s. Thus for that 18 s, the sign captured visual attention 41 percent of the 

time, on average. The supplemental guide signs in condition 6 also received a high proportion of 

visual attention, about 34 percent of the 18 s exposure to that ROI. The specific-service and 

supplemental guide signs in condition 7 were 200 ft (61 m) apart, which resulted in an 

approximate exposure duration of 14 s rather than the 18 s in condition 6. For the 14 s that 

glances at the specific-service sign ROI could be recorded, the signs captured visual attention 

42 percent of the time on average. 
103

Table 37. Mean glance duration in seconds where 0 s was used for no glance.

Condition Sign Type

Mean Glance Duration (s) 

by Order of Appearance

First Second Third

1

Guide 1.04 0.90 0.53

Supplemental guide 1.77 N/A N/A

Specific service 3.11 N/A N/A

2

Guide 0.96 0.94 0.56

Supplemental guide 1.00 N/A N/A

Specific service 3.39 N/A N/A

3

Guide 1.39 1.28 0.84

Supplemental guide 1.63 N/A N/A

Specific service 3.26 N/A N/A

4

Guide 0.92 0.85 0.58

Supplemental guide 0.95 1.04 N/A

Specific service 3.39 N/A N/A

5

Guide 1.28 1.25 0.79

Supplemental guide 1.63 1.96 N/A

Specific service 2.16 3.18 N/A

6

Guide 1.11 1.35 0.83

Supplemental guide 6.07 N/A N/A

Specific service 7.43 N/A N/A

7

Guide 0.69 1.49 0.81

Supplemental guide 4.47 N/A N/A

Specific service 5.88 N/A N/A

N/A = Not applicable. No sign existed.

Table 38 provides mean glance durations without averaging in zero durations. As would be 

expected, these means were slightly longer than those shown in table 37. However, the patterns 

remained the same. Specific-service signs garnered glances more than twice as long as those 

directed at guide signs, and guide sign glance durations were brief. Where there were two 

destinations on advance and supplemental guide signs (conditions 3 and 5), glance durations 

were not substantially greater than when there was only one destination (conditions 1, 2, and 4).
104

Table 38. Mean glance duration in seconds for sign ROIs given a glance occurred.

Condition Sign Type

Mean Glance Duration (s) 

by Order of Appearance

First Second Third

1

Guide 1.18 1.04 0.70

Supplemental guide 1.91 N/A N/A

Specific service 3.63 N/A N/A

2

Guide 1.08 1.06 0.75

Supplemental guide 1.12 N/A N/A

Specific service 3.51 N/A N/A

3

Guide 1.50 1.39 1.00

Supplemental guide 1.74 N/A N/A

Specific service 3.34 N/A N/A

4

Guide 1.05 1.02 0.78

Supplemental guide 1.10 1.22 N/A

Specific service 3.53 N/A N/A

5

Guide 1.41 1.45 0.92

Supplemental guide 1.74 2.05 N/A

Specific service 2.30 3.33 N/A

6

Guide 1.17 1.46 1.04

Supplemental guide 6.15 N/A N/A

Specific service 7.46 N/A N/A

7

Guide 0.76 1.65 0.95

Supplemental guide 4.50 N/A N/A

Specific service 5.96 N/A N/A

N/A = Not applicable. No sign of this type was presented.

To further explore glance behavior, each 0.0083-s gaze vector was assigned to one of 

three object categories: signs, road ahead, and other. The other category included all gaze vectors 

with the forward 39- by 24-degree scene that were not on a sign or road ahead ROI. Such hits 

could be toward other driving-related information (e.g., traffic) or non-driving-related 

information (e.g., billboards). Hits on any guide sign, supplemental guide sign, or specificservice sign were included in the sign category. 

The proportion of gaze to the three gaze categories was analyzed twice. The first analysis 

included the entire distance between the on-ramp merge area and exit gore. The second analysis 

focused on the area around the first advance guide sign so that any effect that resulted from 

increasing the number of signs and decreasing the space between signs would be maximized. 

Specifically, this second analysis spanned an area that began at the end of the entrance merge 

area and ended with last glance at a sign ROI before reaching the second advance guide sign.

The proportions of gaze were analyzed using a GEE model that assumed a gamma response 

distribution and identify link function. The response variable was proportion of time. Predictors 

in the model included object category (sign, road ahead, or other), signing condition, and the 

object-condition interaction. Significance was evaluated with Wald 2 statistics. Because the 

research design was not factorial, conditions were analyzed in pairwise fashion. The number of 

χ
105

pairwise comparisons was large, which could increase the probability of type 1 errors

(i.e., finding a significant effect due to chance variation in means that are not different in the 

population). To protect against this increase in type 1 errors due to alpha inflation, a p criterion 

of 0.002 was used rather than the traditional 0.05.

Condition 1 Versus 2—Effect of Addition of Specific-Service and Supplemental Guide 

Signs:

Condition 1 had no supplemental guide signs or specific-service signs except 2 participants for 

whom 2 out of 21 exits were designated with one of these signs. Condition 2 had one lodging 

sign and one supplemental guide sign, and the guide signs listed just one destination. Therefore, 

condition 1 represent a test of the effect of adding a specific-service and supplemental guide 

sign.

Category ( 2

(2) = 201.00, p < 0.001), condition ( 2(1) = 96.17, p < 0.001), and the interaction 

( 2

(2) = 105.38, p < 0.001) were statistically significant. Because the interaction was significant, 

main effects are not discussed. Table 39 displays summary statistics for the comparison of 

conditions 1 and 2 where the response variable covered the entire data collection zone.

Table 39. Comparison of the GEE estimates proportion of gaze time between

conditions 1 and 2 for the entire data collection zone.

Category

Condition 1 Condition 2

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.45 0.48 0.52 0.44 0.47 0.50

Road ahead 0.45 0.49 0.52 0.41 0.44 0.48

Signs 0.03 0.03 0.04 0.08 0.09 0.10

As can be seen in table 40, the proportion of time to other was relatively constant, whereas there 

was a tradeoff between the proportion of time to road ahead and signs: proportion of time to road 

ahead decreased from condition 1 to 2, and the proportion of time to signs increased. 

As with the full data collection zone analysis, when only the area around the first advance sign is 

considered, category ( 2(2) = 150.80, p < 0.001), condition ( 2(1) = 82.06, p < 0.001), and the 

second-order interaction ( 2(2) = 129.10, p < 0.001) were significant. In this second analysis, the 

same tradeoff between road ahead and signs is evident, but more pronounced.

χ χ

χ

χ χ

χ
106

Table 40. Comparison of the GEE estimates of the proportion of gaze between 

conditions 1 and 2 for the area around the first advance guide sign.

Category

Condition 1 Condition 2

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.45 0.49 0.53 0.45 0.47 0.50

Road ahead 0.43 0.46 0.50 0.33 0.36 0.40

Signs 0.04 0.05 0.06 0.15 0.17 0.18

Condition 2 Versus 3—Effect of Adding a Second Destination to Guide Signs:

Conditions 2 and 3 had the same number of signs, but the guide and supplemental guide signs in 

condition 3 listed two destinations. As can be seen in table 41 and table 42, the effect of adding 

destinations to the guide signs was to decrease the proportion of gaze time to the road ahead and 

perhaps to other. The interaction between category and condition was significant for both the 

entire data collection zone, 2(2) = 51.54, p < 0.001, and the portion of the data collection zone 

around the first guide sign, 2

(2) = 34.74, p < 0.001.

Table 41. Comparison of the GEE estimates proportion of gaze between conditions 2 and 3 

for the entire data collection zone.

Category

Condition 2 Condition 3

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.44 0.47 0.50 0.44 0.47 0.50

Road ahead 0.41 0.44 0.48 0.40 0.43 0.46

Signs 0.08 0.09 0.10 0.10 0.11 0.12

Table 42. Comparison of the GEE estimates of the proportion of gaze between 

conditions 2 and 3 for the area around the first advance guide sign.

Category

Condition 2 Condition 3

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.45 0.47 0.50 0.43 0.46 0.49

Road ahead 0.33 0.36 0.40 0.32 0.35 0.39

Signs 0.15 0.17 0.18 0.17 0.19 0.21

Condition 2 Versus 4—Effect of Adding a Second Supplemental Guide Sign:

The difference between conditions 2 and 4 was that condition 2 had only one supplemental guide 

sign, whereas condition 4 had two supplemental guide signs. All signs were 800 ft (244 m) apart 

with both supplemental guide signs coming after the first advance guide sign. 

χ

χ
107

As can be seen in table 43, there was a slight increase in the proportion of gaze time at the signs 

and a slight decrease in the proportion of time at the road ahead when there were two 

supplemental guide signs rather than one. This resulted in a significant interaction of ROI 

category and condition, 2

(2) = 17.33, p < 0.001. When only the partial data collection zone was 

considered, the interaction effect was not statistically significant.

Table 43. Comparison of GEE estimates of the proportion of gaze between 

conditions 2 and 4 for the entire data collection zone.

Category

Condition 2 Condition 4

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.44 0.47 0.50 0.45 0.48 0.51

Road ahead 0.41 0.44 0.48 0.39 0.42 0.46

Signs 0.08 0.09 0.10 0.09 0.10 0.11

Condition 3 Versus 5—Addition of a Second Specific-Service Sign and a Second 

Supplemental Guide Sign:

Conditions 3 and 5 both had two destinations listed on the guide signs. The difference between 

conditions 3 and 5 was that condition 3 had one specific-service sign and one supplemental guide 

sign, whereas condition 5 had two signs of each of those types. The second supplemental guide 

sign came 800 ft (244 m) after the second advance guide sign.

As can be seen in table 44, there was a tradeoff between the proportion of gaze time at the road 

and at signs. With the increase in the number of specific-service and supplemental guide signs,

the signs captured more visual attention at the expense, primarily, of gaze at the road ahead. The 

interaction between condition and category was significant, 2(2) = 151.85, p < 0.001.

Table 44. Comparison of GEE estimates of the proportion of gaze between conditions 3 and 

5 for the entire data collection zone.

Category

Condition 3 Condition 5

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.44 0.47 0.50 0.43 0.46 0.49

Road ahead 0.40 0.43 0.46 0.36 0.39 0.42

Signs 0.10 0.11 0.12 0.14 0.15 0.17

The partial data collection zone did not include the second supplemental guide sign. Thus, the 

partial data collection zone comparison was actually a test of the effect of adding a second 

specific-service sign.

As can be seen in table 45, signs captured about 6-percent more of the proportion of gaze when 

there were two specific-service signs rather than one. This increase in the proportion of gaze at

χ

χ
108

the signs came at the expense of both the other and road ahead categories. The interaction 

between category and condition was statistically significant, 2

(2) = 117.79, p < 0.001.

Table 45. Comparison of GEE estimates of the proportion of gaze between 

conditions 3 and 5 for the partial data collection zone.

Category

Condition 3 Condition 5

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.43 0.46 0.49 0.40 0.43 0.46

Road ahead 0.32 0.35 0.39 0.29 0.32 0.35

Signs 0.17 0.19 0.21 0.23 0.25 0.28

Condition 5 Versus 6—Effect of Adding a Third Specific Service and Supplemental Guide 

Signs and Reducing Distance Between These Signs to 400 ft (122 m):

Because the data collection zones were all the same length, adding a third specific-service sign 

and a third supplemental guide sign required reducing the distance between signs. In condition 4,

the signs were all 800 ft (244 m) apart and thus were compliant with the MUTCD sign spacing 

requirement.(2) In condition 5, all signs prior to the second advance service sign were 400 ft

(244 m) apart. Two destinations were listed on all guide signs in both conditions. The third 

specific-service sign was a gas sign that followed the lodging sign.

As can be seen in table 46, increasing the number of signs and decreasing the distance between 

signs increased the proportion of gaze at the signs at the expense of both the road ahead and 

other. The interaction between category and condition was statistically significant, 

2

(2) = 133.73, p < 0.001. All of the differences between conditions 5 and 6 were in the partial 

data collection zone around the first advance guide sign. As can be seen in table 47, the 

proportion of gaze at the signs in this area increased by about 14 percent at the expense of both 

the road ahead (5 percent) and other (10 percent). The category by condition interaction was 

statistically significant, 2

(2) = 280.81, p < 0.001.

Table 46. Comparison of GEE estimates of the proportion of gaze between 

conditions 5 and 6 for the entire data collection zone.

Category

Condition 5 Condition 6

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.43 0.46 0.49 0.39 0.41 0.44

Road ahead 0.36 0.39 0.42 0.35 0.38 0.41

Signs 0.14 0.15 0.17 0.20 0.21 0.23

χ

χ

χ
109

Table 47. Comparison of the GEE estimates of the proportion of gaze between

conditions 5 and 6 for the partial data collection zone.

Category

Condition 5 Condition 6

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.40 0.43 0.46 0.31 0.33 0.36

Road ahead 0.29 0.32 0.35 0.25 0.27 0.31

Signs 0.23 0.25 0.28 0.37 0.39 0.42

Condition 6 Versus 7—Effect of Reducing Distance Between Signs from 400 ft (122 m) to 

200 ft (61 m):

Conditions 6 and 7 were the same except for the distance between signs. In condition 7, the signs 

were 200 ft (61 m) apart. Because the differences between conditions were in the partial data 

collection zone, only the partial data collection zone results are presented. As can be seen in

table 48, decreasing the distance between signs decreased the proportion of gaze at the signs. 

This effect may be because the data collection zone was only 600 ft (183 m) shorter in 

condition 7 than in condition 5, but the area containing the signs was 1,200 ft (366 m) smaller in 

condition 7. Thus, in condition 7, the proportion of time the signs were legible was reduced 

compared with condition 6. The reduction in the proportion of gaze time at the signs from 

condition 6 to 7 was statistically significant as indicated by the significant interaction between 

category and condition, 2

(2) = 44.28, p < 0.001.

Table 48. Comparison of GEE estimates of the proportion of gaze between

conditions 6 and 7 for the partial data collection zone. 

Category

Condition 6 Condition 7

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.31 0.33 0.36 0.33 0.35 0.38

Road ahead 0.25 0.27 0.31 0.27 0.30 0.33

Signs 0.37 0.39 0.42 0.33 0.35 0.37

Summary of Paired Gaze Comparisons:

As signs were added and the space between signs was reduced, the proportion of time attending 

to the road ahead, as defined by the road ahead ROI, went down, and the proportion of gaze to 

signs increased. Table 49 shows how the proportion of time that the gaze vector fell on signs as a 

function of condition. Consistent with the duration of gaze data in table 38, the biggest increases 

in gaze at signs came when specific-service logo signs were added: from none to one 

(conditions 1 to 2), from one to two (condition 2 to 5), and from two to three (conditions 5 to 6).

χ
110

Table 49. Summary of findings for proportion of gaze to signs.

Condition Condition Description

Estimated Mean 

Proportion of 

Time to Signs

1 No specific-service or supplemental signs 0.03

2 One specific-service and supplemental sign 0.09

3 Add second destination to signs 0.11

4 Add second supplemental sign (one destination per sign) 0.10

5 Two specific-service and supplemental signs (two destinations) 0.15

6 Three specific-service and supplemental signs (400 ft (122 m)

separation)

0.39

7 Three specific-service and supplemental signs (200 ft (61 m)

separation)

0.35

Fixation Results

As in the previous simulator experiments, a fixation was defined as seven consecutive gaze 

positions (60 ms) within a fixation radius of 4 percent of the vertical image height (i.e., 29 pixels 

on a 720-pixel image) and centered on the first of the seven gaze positions that designated the

start of a fixation. The fixation continued until there were six consecutive hits (50 ms) outside 

the fixation radius. For a simulated object 500 ft (152 m) ahead, the fixation radius subtended a 

visual angle of about 2 degrees. A fixation on an ROI was recorded if the center of the fixation 

was on the ROI.

The mean probability of at least one fixation falling on a sign ROI is shown in table 50. The 

probability of fixating on any particular guide sign (advance or exit) was somewhat lower than 

for specific-service and supplemental signs, perhaps because participants realized the guide sign 

information was redundant across the three signs and fixations on any one or two of the guide 

signs could be sloughed off without loss of information.
111

Table 50. Probability of fixating on each ROI category as a function of condition.

Condition Sign Type

Probability of Fixation by 

Order of Appearance

First Second Third

1

Guide 0.77 0.77 0.61

Supplemental guide 0.86 N/A N/A

Specific service 0.86 N/A N/A

2

Guide 0.78 0.75 0.61

Supplemental guide 0.78 N/A N/A

Specific service 0.93 N/A N/A

3

Guide 0.84 0.82 0.72

Supplemental guide 0.86 N/A N/A

Specific service 0.93 N/A N/A

4

Guide 0.75 0.71 0.66

Supplemental guide 0.74 0.74 N/A

Specific service 0.94 N/A N/A

5

Guide 0.80 0.79 0.71

Supplemental guide 0.87 0.91 N/A

Specific service 0.86 0.93 N/A

6

Guide 0.86 0.83 0.68

Supplemental guide1 0.97 N/A N/A

Specific service1 0.98 N/A N/A

7

Guide 0.77 0.85 0.74

Supplemental guide1 0.98 N/A N/A

Specific service1 0.97 N/A N/A

N/A = Not applicable. No second or third ROI.

1

One ROI covered three signs from category.

The mean number of fixations as a function of condition and ROI category is shown in table 51. 

Note that the number of fixations on single lodging service signs is roughly twice that for 

supplemental guide signs, and food and gas signs received only slightly fewer fixations than the 

lodging signs. Table 52 shows the mean duration of each fixation on an ROI. For the specificservice signs in condition 6, the product of the average number of fixations multiplied by the 

average duration of fixations yields a total duration of fixation on that ROI of 7.68 s. With two 

specific-service signs in condition 4, the sum of the average fixation duration times their 

respective mean frequencies was 5.81 s. For a single specific-service sign in condition 2, the total 

fixation duration was 3.65 s. Although participants only needed to search the lodging sign, it is 

evident that food and gas signs added to their search time.
112

Table 51. Mean number of fixations as a function of condition and ROI category.

Condition Sign Type

Mean Probability of 

Fixation by Order of 

Appearance

First Second Third

1

Guide 2.84 2.36 1.59

Supplemental guide 4.21 N/A N/A

Specific service 7.79 N/A N/A

2

Guide 2.52 2.53 1.54

Supplemental guide 2.64 N/A N/A

Specific service 8.12 N/A N/A

3

Guide 3.44 3.15 2.13

Supplemental guide 4.19 N/A N/A

Specific service 8.06 N/A N/A

4

Guide 2.36 2.28 1.63

Supplemental guide 2.41 2.68 N/A

Specific service 8.19 N/A N/A

5

Guide 3.21 3.16 2.01

Supplemental guide 4.13 4.93 N/A

Specific service 5.51 8.00 N/A

6

Guide 3.09 2.27 2.06

Supplemental guide 15.41 N/A N/A

Specific service 18.72 N/A N/A

7

Guide 2.08 3.57 2.13

Supplemental guide 12.61 N/A N/A

Specific service 15.44 N/A N/A

NA = Not applicable. No second or third ROI.
113

Table 52. Mean fixation duration on ROIs given at least one fixation.

Condition Sign Type

Mean Fixation Duration (s) 

by Order of Appearance

First Second Third

1

Guide 0.42 0.40 0.38

Supplemental Guide 0.46 N/A N/A

Specific Service 0.40 N/A N/A

2

Guide 0.42 0.41 0.38

Supplemental Guide 0.41 N/A N/A

Specific Service 0.45 N/A N/A

3

Guide 0.46 0.42 0.41

Supplemental Guide 0.40 N/A N/A

Specific Service 0.45 N/A N/A

4

Guide 0.45 0.38 0.41

Supplemental Guide 0.42 0.42 N/A

Specific Service 0.47 N/A N/A

5

Guide 0.46 0.46 0.42

Supplemental Guide 0.45 0.43 N/A

Specific Service 0.43 0.43 N/A

6

Guide 0.42 0.46 0.47

Supplemental Guide 0.41 N/A N/A

Specific Service 0.41 N/A N/A

7

Guide 0.35 0.45 0.43

Supplemental Guide 0.34 N/A N/A

Specific Service 0.39 N/A N/A

N/A = Not applicable. No second or third ROI.

The proportion of fixations on the three categories of ROI, sign, road ahead, and other were 

analyzed in the same manner as the gaze vector hits on ROIs. As before, a GEE model was used 

that assumed a gamma distribution and identify link function. Significance was evaluated with a 

0.002 alpha criterion.

Condition 1 Versus 2—The Effect of Adding One Specific-Service and One Supplemental 

Guide Sign:

As can be seen in table 53, adding two signs—a lodging sign and a supplemental guide sign—

more than doubled the number of fixations on signs and reduced fixations on the road ahead. 

This interaction effect was statistically significant, 2(2) = 89.63, p < 0.001. When looking only 

at the partial data collection zone, the results were similar, with a higher proportion of fixations 

on the signs compared with the road ahead, and the interaction effect was again significant.

χ
114

Table 53. Estimated mean proportion of fixations on signs, road ahead, and other as a 

function of signing condition over the entire data collection zone.

Category

Condition 1 Condition 2

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.28 0.32 0.37 0.28 0.31 0.35

Road ahead 0.60 0.64 0.68 0.54 0.58 0.62

Signs 0.05 0.05 0.06 0.10 0.11 0.13

Condition 2 Versus 3—The Effect of Adding a Second Destination:

As can be seen in table 54, which shows the proportion of fixations over the entire data 

collection zone, the addition of a second destination on the guide signs added about 3 percent to 

the proportion of fixations on signs and reduced the proportion of road ahead fixations by a 

similar amount. This resulted in a significant category by condition interaction, 2(2) = 35.95, 

p < 0.001. 

Table 54. Estimated mean proportion of fixations on ROI categories as a function of 

signing conditions 2 and 3 over the entire data collection zone.

Category

Condition 2 Condition 3

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.28 0.31 0.35 0.28 0.31 0.35

Road ahead 0.54 0.58 0.62 0.52 0.56 0.60

Signs 0.10 0.11 0.13 0.13 0.14 0.15

Condition 2 Versus 4—The Effect of Adding a Second Supplemental Guide Sign:

As can be seen in table 55, which shows the proportion of fixations over the entire data 

collection zone, the addition of a second supplemental guide sign added about 2 percent to the 

proportion of fixations on the signs at the expense of fixations on the road ahead and other. This 

resulted in a significant category by condition interaction, 2

(2) = 19.30, p < 0.001.

Table 55. Estimated mean proportion of fixations on ROI categories as a function of 

signing conditions 2 and 4.

Category

Condition 2 Condition 4

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.28 0.31 0.35 0.29 0.32 0.36

Road ahead 0.54 0.58 0.62 0.52 0.56 0.60

Signs 0.10 0.11 0.13 0.12 0.13 0.14

χ

χ
115

Condition 3 Versus 5—The Effect of Adding a Second Specific-Service and Second 

Supplemental Guide Sign:

As can be seen in table 56, which shows the proportion of fixations over the entire data 

collection zone, the addition of a second specific-service sign and a second supplemental guide 

sign added about 6 percent to the proportion of fixations on the signs at the expense of fixations 

on the road ahead. This resulted in a significant category by condition interaction,

2

(2) = 168.56, p < 0.001.

Table 56. Estimated mean proportion of fixations on ROI categories as a function of 

signing conditions 3 and 5.

Category

Condition 3 Condition 5

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.28 0.31 0.35 0.27 0.31 0.35

Road ahead 0.52 0.56 0.60 0.47 0.50 0.55

Signs 0.13 0.14 0.15 0.18 0.20 0.22

Condition 5 Versus 6—The Effect of Adding a Third Specific-Service Sign and a Third 

Supplemental Guide Sign and Reducing Distance Between Signs to 400 ft (122 m):

As can be seen in table 57, which shows the proportion of fixations over the entire data 

collection zone, the addition of a third specific-service sign and a third supplemental guide sign 

added about 7-percent more fixations to the signs at the expense of fixations on other and the 

road ahead. This resulted in a significant category by condition interaction, 2

(2) = 173.95, 

p < 0.001.

Table 57. Estimated mean proportion of fixations on ROI categories as a function of 

signing conditions 5 and 6.

Category

Condition 5 Condition 6

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.27 0.31 0.35 0.23 0.25 0.29

Road ahead 0.47 0.50 0.55 0.45 0.49 0.53

Signs 0.18 0.20 0.22 0.25 0.27 0.29

Condition 6 Versus 7: The Effect of Reducing the Distance Between Signs From 400 ft 

(122 m) to 200 ft (61 m):

As can be seen in table 58, which shows the proportion of fixations over the entire data 

collection zone, the reduction in the distance between signs resulted in a 4-percent decrease in 

the proportion of fixations on signs to the benefit of the road ahead and other. This resulted in a 

significant category by condition interaction, 2(2) = 101.26, p < 0.001.

χ

χ

χ
116

Table 58. Estimated mean proportion of fixations to the ROI categories as a function of 

signing conditions 6 and 7.

Category

Condition 6 Condition 7

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.23 0.25 0.29 0.24 0.27 0.31

Road ahead 0.45 0.49 0.53 0.47 0.51 0.55

Signs 0.25 0.27 0.29 0.21 0.23 0.24

Although the trends and statistically significant effects were the same for the proportion of 

fixations in the full data collection zone and the partial data collection zone, the magnitude of the 

reduction in fixations on the road ahead is more striking when only the area around the first 

advance guide sign is considered. In the partial data collection zone for condition 6 (see

table 59), which included more than 3,300 ft (396 m) of travel distance, the road ahead captured 

only 34 percent of fixations compared with the signs, which captured, on average, almost half the 

fixations.

Table 59. Partial data collection zone estimates of the mean proportion of fixations on ROI 

categories as a function of signing conditions 6 and 7.

Category

Condition 6 Condition 7

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Lower 

Confidence 

Limit Mean

Upper 

Confidence 

Limit

Other 0.16 0.18 0.20 0.18 0.20 0.23

Road ahead 0.30 0.34 0.38 0.32 0.35 0.40

Signs 0.45 0.47 0.51 0.41 0.43 0.46

Driving Performance Measures

Measures of the steering wheel position were not available for this study because of data 

collection problems related to a change in the driving simulator hardware.

Vehicle speed was analyzed for the partial data collection zones. It was hypothesized that if the 

signs caused information overload, drivers might slow to give themselves more time to search 

for the assigned destinations. On average, participants drove at the instructed speed of 65 mi/h 

(105 km/h). GEE models were used to test for difference in speed as a function of condition. A 

Gaussian response distribution with identity link function was assumed. Using an alpha level of 

0.05, which does not adjust for experiment-wise error rate, there were two significant reductions 

in speed that were consistent with the hypothesis that information demand caused a speed 

reduction. The planned comparisons that were examined are shown in table 60. Estimated mean 

speed in condition 6 was significantly slower than estimated mean speed in condition 5, 

2

(1) = 5.36, p = 0.021, and estimated mean speed in condition 2 was significantly faster than 

estimated mean speed in condition 7, 2 (1) = 4.89, p = 0.027.

χ

χ
117

Table 60. Planned between-condition vehicle speed comparisons.

Conditions Compared

Speed 

Reduction 

Significant?

1—No specific-service or 

supplemental guide signs.

2—One specific-service and 

one supplemental guide sign.

ns

2—One specific-service and 

one supplemental guide sign.

3—One specific-service and 

one supplemental guide sign. 

Two destinations per guide sign.

ns

2—One specific-service and 

one supplemental guide sign.

4—One specific-service and 

two supplemental guide signs.

ns

3—One specific-service and 

one supplemental guide sign. 

Two destinations per guide sign. 

5—Two specific-service and 

two supplemental guide signs. 

Two destinations per guide sign.

ns

5—Two-specific service and 

two supplemental guide signs. 

Two destinations per guide sign.

6—Three specific-service and 

three supplemental guide signs. 

Two destinations per guide sign.

p = 0.021

6—Three specific-service and 

three supplemental guide signs. 

Two destinations per guide sign. 

Distance between signs: 400 ft 

(122 m).

7—Three specific-service and 

three supplemental guide signs. 

Two destinations per guide sign. 

Distance between signs: 200 ft (61 m).

ns

2—One specific-service and 

one supplemental guide sign.

7—Three specific-service and 

three supplemental guide signs. 

Two destinations per guide sign. 

Distance between signs: 400 ft (61 m).

p = 0.027

ns = Not significant.

Table 61 shows the estimated mean speed in the partial data collection zones as a function of 

signing condition. Although statistically significant differences were identified, all differences 

were less than 1 mi/h (1.61 km/h).

Table 61. Vehicle estimated mean speed (in mi/h) in the partial data collection zones as a 

function of signing condition.

Condition

Mean

(mi/h)

95-Percent 

Confidence Limits

(mi/h)

1 65.40 65.00–65.80

2 65.28 64.92–65.65

3 65.31 64.88–65.74

4 65.46 65.05–65.86

5 65.32 64.90–65.73

6 65.07 64.64–65.49

7 64.96 64.52–65.40

1 mi/h = 1.61 km/h
118

DISCUSSION

Participants in this experiment were given a task that is probably rare in the real world—to watch 

for a guide sign destination, a supplementary guide sign destination, or a hotel on a specificservice sign. Both the eye-tracking data and the exit-taking behavior suggest that the participants 

took the task seriously. The mean speed data suggest, however, that while monitoring the signs 

for the assigned destination, the participants continued to monitor the driving task sufficient to 

maintain the instructed speed.

The eye-tracking results appear to support the current MUTCD standards and guidance on the 

frequency and spacing of guide signs.(2) All additional signing resulted in increased eyes-off-road 

time. In particular, specific-service signs appear to be problematic. All specific-service signs had

six logos. On average, participants fixated on a lone lodging sign for 3.65 s, with an average of 

about eight fixations that averaged 0.4 s each. Each additional logo sign added about 2 s of total 

fixation time, even though the additional signs were not lodging signs and did not require a 

search beyond noting the panel legend. When the fixation data were scored, it appeared that most

participants searched all the specific-service signs for the Holiday Inn® logo. This suggests that 

the sign legends (i.e., food, lodging, and gas) had a minimal effect on search strategy. 

The difficulty participants had in searching the logo signs may also be related to the 

distinguishability of the logos. Recent research that addressed the question of the maximum 

number of logos on a single specific-service sign used a reaction time paradigm to determine the 

amount of time it takes to detect a desired business logo on signs with four, six, or nine logos.(63)

Participants’ only task was to watch a video monitor and strike a key labeled “yes” if a particular 

logo was on the sign or a key labeled “no” if it was not. From the time the sign appeared until the 

participants pressed one of keys was the dependent measure. Mean reaction times for participants 

less than 50 years of age were 1.3 s, 1.6 s, and 2.2 s for four-, six-, and nine-panel signs,

respectively. Based on the generally cited criterion that glances away from the road of more than 

2 s are unsafe, the investigators recommend that nine-panel logo signs not be approved.(64,20)

Two key assumptions underlie this recommendation. One is that looking at logo signs constitutes 

a glance away from the roadway. For most of the 10-s approach to the logo signs for which 

fixation data were collected in the present study, the signs were within 10 degrees of the 

participants’ forward field of view. In naturalistic driving studies, these would not be considered 

glances away from the forward roadway, because in naturalistic driving studies, which to date 

have not used eye tracking technology, gaze direction measurement is accurate to a radius of 

20 degrees.

(65) By this criterion, 3.65 s might not be an unsafe amount of time to gaze at a 

roadside sign. Furthermore, although total fixation and gaze times at the specific-service signs 

were long, individual glances away from the roadway were general short—less than 0.5 s. 

The other key assumption of the laboratory reaction time study was that the reaction time for 

deciding whether a particular logo is on a sign is an indicator of the amount of time a driver on 

the roadway would search for a logo on a sign. This assumption may be true in a relative sense, 

(i.e., longer laboratory reaction times indicate longer on-road search time). The present results 

suggest that the laboratory reaction time findings are not indicative of the absolute search time in 

a driving context. Search times on the six-panel signs in this study were more than twice the 

decision reaction times in the laboratory.
119

Whether search times of 3.7 s (one six-panel sign), 5.8 s (two six-panel signs) or 7.7 s 

(three six-panel signs) represent a distraction is not clear, although the search times should raise 

concerns, particularly when they represent close to half of the fixation time over more than 

0.5 mi (0.8 km) of travel distance. A simulator study that presents various roadway hazards while 

participants search for specified logos might clarify this issue.

It is possible that with more distinctive logos, search times could be reduced. The target logo in 

the present study was light green on a white background. There were several logos, although 

none were lodging logos, with similar white backgrounds and low-contrast green images. 

However, many businesses have logos with low contrast and fine details that are not easily 

identified. Some national chains have logos that may be quickly identified at distances of 

1,000 ft (305 m), but these are not the norm. In that sense, the present search task was a 

representative one. The MUTCD does not control the design of business logos, only the 

maximum size of logo panels on specific-service signs.(2)

Total fixation durations (number of fixations multiplied by mean fixation duration) for 

supplemental guide signs were less than 2 s when there was one supplemental sign containing 

either one or two destinations. With two supplemental guide signs with a single destination on 

each, the sum of fixation times was about 0.5 s longer than for two destinations on one sign. This 

suggests that combining two destinations on one sign, the recommended practice in MUTCD, is 

a better choice than using two signs for those destinations.(2) When there were two or more 

supplemental guide signs with two destinations on each, total mean fixation time ranged between 

about 4 and 6.3 s.

SUMMARY AND CONCLUSIONS

The MUTCD standards for supplemental guide signs are strongly supported by this research.

The findings with respect to specific-service signs are more problematic. The results certainly 

support not exceeding the current standard of no more than six logos per sign and no more than 

four signs with minimum separations of 800 ft (244 m). Methods for increasing the conspicuity 

of specific-service sign legends should be explored so that drivers do not unnecessarily search 

signs that list services that they are not seeking.

121

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